Carbon Chemistry analysis: seasonality and long trends
Ana Palacio-Castro
2023-03-26
Libraries and settings
1. Import and format carbon chemistry data
Seasonality
- Winter: Jan-Mar
- Spring: Apr-Jun
- Summer: Jul-Sep
- Fall: Oct-Dec
- Wet: May - October
- Dry: November - April
2. Check data available
2.1 Locations (all samples)
Figure Supp: Map of the sites where individual samples were collected
Notes: * Check location of individual SiteIDs
2.2 Average GPS point location
Flag samples with coordinates that seem off from the permanent station
# Coordinates<-select(FLK.data, "CTDID", "Latitude", "Longitude", "SiteID")
# Coordinates<-join(Coordinates, WS.GPS.Sites,
# type = "left", by="SiteID")
# Coordinates$Lat_diff <- abs(Coordinates$Latitude-Coordinates$Lat)
# Coordinates$Lon_diff <- abs(Coordinates$Longitude-Coordinates$Lon)
# Check.coordinates1 <- Coordinates[(Coordinates$Lat_diff>0.02), ]
# Check.coordinates2 <- Coordinates[(Coordinates$Lon_diff>0.02), ]
# Check.coordinates <- rbind(Check.coordinates1, Check.coordinates2)
# Check.coordinates <- unique(Check.coordinates)
# #write.csv(Check.coordinates, "FLK_results/2_WrongGPS_points.csv", row.names=F)- Figure 1: Stations
Figure 1: Location of the permanent sampling stations along the Florida Reef Tract. The black boxes delimitate the biogeographical regions. The color of the stations represents their position along the shelf.
2.3 Number of samples
By zone and location
| Sub_region | Inshore | Mid channel | Offshore | Oceanic |
|---|---|---|---|---|
| BB | 84 | 76 | 83 | |
| UK | 107 | 71 | 111 | 37 |
| MK | 129 | 129 | 130 | 83 |
| LK | 138 | 138 | 171 | 87 |
By year and sub_region
| Sub_region | 2010 | 2011 | 2012 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| BB | 4 | 14 | 3 | 6 | 30 | 36 | 30 | 35 | 24 | 24 | 37 |
| UK | 7 | 19 | 3 | 8 | 45 | 51 | 42 | 45 | 30 | 31 | 45 |
| MK | 15 | 51 | 9 | 11 | 55 | 66 | 55 | 56 | 44 | 44 | 65 |
| LK | 18 | 61 | 9 | 11 | 58 | 72 | 60 | 76 | 53 | 46 | 70 |
By climatological season and region
| Sub_region | Fall | Spring | Summer | Winter |
|---|---|---|---|---|
| LK | 149 | 133 | 129 | 123 |
| MK | 133 | 120 | 121 | 97 |
| UK | 88 | 82 | 84 | 72 |
| BB | 68 | 60 | 63 | 52 |
By Month-year
| 2010 | 2011 | 2012 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | Months |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | 38 | 19 | 6 | 35 | January | ||||||
| 16 | 22 | 38 | February | ||||||||
| 24 | 2 | 38 | 35 | 33 | March | ||||||
| 24 | 38 | 16 | 35 | 37 | April | ||||||
| 38 | 16 | 28 | May | ||||||||
| 20 | 24 | 38 | 38 | 35 | 8 | June | |||||
| 37 | 38 | 18 | 36 | 25 | July | ||||||
| 28 | 20 | 38 | 35 | 10 | August | ||||||
| 38 | 38 | 36 | September | ||||||||
| 28 | 38 | 18 | 38 | 36 | October | ||||||
| 37 | 35 | 38 | November | ||||||||
| 25 | 36 | 37 | 37 | 35 | December |
By Season-year
| 2010 | 2011 | 2012 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | Season |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 24 | 16 | 24 | 76 | 73 | 52 | 6 | 35 | 38 | Winter | ||
| 20 | 48 | 76 | 38 | 38 | 67 | 35 | 73 | Spring | |||
| 28 | 75 | 76 | 38 | 38 | 72 | 35 | 35 | Summer | |||
| 53 | 36 | 37 | 35 | 38 | 55 | 38 | 75 | 71 | Fall |
By Month-region
| Sub_region | Zone | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| BB | Inshore | 7 | 4 | 7 | 9 | 4 | 8 | 10 | 5 | 6 | 8 | 6 | 10 |
| BB | Mid channel | 7 | 3 | 6 | 9 | 4 | 5 | 10 | 5 | 6 | 7 | 6 | 8 |
| BB | Offshore | 7 | 4 | 7 | 9 | 4 | 8 | 10 | 5 | 6 | 8 | 6 | 9 |
| UK | Inshore | 10 | 4 | 9 | 13 | 6 | 8 | 13 | 8 | 7 | 9 | 9 | 11 |
| UK | Mid channel | 7 | 2 | 7 | 6 | 3 | 9 | 9 | 3 | 6 | 6 | 5 | 8 |
| UK | Offshore | 12 | 3 | 9 | 11 | 5 | 12 | 14 | 6 | 9 | 11 | 8 | 11 |
| UK | Oceanic | 3 | 2 | 4 | 3 | 1 | 5 | 4 | 2 | 3 | 4 | 2 | 4 |
| MK | Inshore | 10 | 6 | 9 | 12 | 8 | 15 | 13 | 11 | 9 | 13 | 9 | 14 |
| MK | Mid channel | 9 | 7 | 11 | 12 | 8 | 13 | 13 | 11 | 9 | 13 | 9 | 14 |
| MK | Offshore | 10 | 6 | 11 | 13 | 7 | 13 | 13 | 11 | 9 | 13 | 9 | 15 |
| MK | Oceanic | 6 | 5 | 7 | 7 | 4 | 8 | 8 | 8 | 6 | 8 | 6 | 10 |
| LK | Inshore | 11 | 8 | 12 | 12 | 7 | 16 | 9 | 15 | 9 | 15 | 9 | 15 |
| LK | Mid channel | 12 | 9 | 12 | 12 | 7 | 15 | 10 | 13 | 9 | 15 | 9 | 15 |
| LK | Offshore | 18 | 7 | 13 | 14 | 10 | 18 | 13 | 18 | 12 | 19 | 12 | 17 |
| LK | Oceanic | 7 | 6 | 8 | 8 | 4 | 10 | 5 | 10 | 6 | 9 | 5 | 9 |
Per station during all sampling time
| 2010 | 2011 | 2012 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | SiteID | Sub_region | Zone |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | 6 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | 1 | BB | Inshore |
| 2 | 6 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | 3 | BB | Offshore |
| 1 | 4 | 1 | 5 | 6 | 5 | 4 | 2 | 3 | 4 | 4 | UK | Inshore | |
| 1 | 4 | 1 | 1 | 5 | 6 | 5 | 4 | 2 | 3 | 4 | 5 | UK | Inshore |
| 2 | 2 | 1 | 1 | 5 | 5 | 4 | 4 | 3 | 3 | 4 | 5.5 | UK | Mid channel |
| 2 | 4 | 1 | 5 | 5 | 4 | 4 | 3 | 3 | 5 | 6 | UK | Offshore | |
| 1 | 5 | 1 | 1 | 5 | 5 | 4 | 4 | 3 | 3 | 5 | 6.5 | UK | Oceanic |
| 2 | 5 | 1 | 1 | 5 | 6 | 5 | 5 | 4 | 4 | 5 | 10 | MK | Inshore |
| 1 | 4 | 1 | 1 | 5 | 6 | 5 | 4 | 4 | 4 | 6 | 13 | MK | Inshore |
| 2 | 6 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | 7 | MK | Inshore | |
| 2 | 4 | 1 | 1 | 5 | 6 | 5 | 5 | 4 | 4 | 6 | 11 | MK | Mid channel |
| 1 | 3 | 1 | 1 | 5 | 6 | 5 | 5 | 4 | 4 | 6 | 14 | MK | Mid channel |
| 2 | 5 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | 8 | MK | Mid channel |
| 1 | 5 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | 12 | MK | Offshore |
| 2 | 6 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | 9 | MK | Offshore | |
| 2 | 6 | 1 | 1 | 5 | 6 | 5 | 4 | 4 | 4 | 6 | 9.5 | MK | Oceanic |
| 2 | 6 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 5 | 16 | LK | Inshore |
| 2 | 6 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | 19 | LK | Inshore | |
| 2 | 6 | 1 | 1 | 5 | 6 | 5 | 7 | 5 | 4 | 6 | 24 | LK | Inshore |
| 2 | 6 | 1 | 1 | 5 | 6 | 5 | 7 | 4 | 4 | 5 | 17 | LK | Mid channel |
| 2 | 5 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | 20 | LK | Mid channel |
| 1 | 6 | 1 | 1 | 5 | 6 | 5 | 7 | 5 | 4 | 6 | 23 | LK | Mid channel |
| 2 | 6 | 1 | 1 | 5 | 6 | 5 | 7 | 4 | 4 | 6 | 18 | LK | Offshore |
| 1 | 5 | 1 | 5 | 6 | 5 | 5 | 4 | 4 | 6 | 21LK | LK | Offshore | |
| 1 | 3 | 1 | 1 | 5 | 6 | 5 | 7 | 5 | 4 | 6 | 22 | LK | Offshore |
| 2 | 6 | 1 | 1 | 4 | 6 | 5 | 5 | 4 | 3 | 6 | 21.5 | LK | Oceanic |
| 1 | 6 | 1 | 1 | 4 | 6 | 5 | 6 | 5 | 3 | 6 | 22.5 | LK | Oceanic |
| 2 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | 2 | BB | Mid channel | |
| 4 | 1 | 1 | 5 | 6 | 5 | 5 | 4 | 4 | 6 | 15 | MK | Offshore | |
| 3 | 1 | 1 | 5 | 6 | 5 | 4 | 4 | 4 | 6 | 15.5 | MK | Oceanic | |
| 1 | 5 | 6 | 5 | 6 | 4 | 4 | 7 | EK_IN | BB | Inshore | |||
| 1 | 5 | 6 | 5 | 5 | 4 | 4 | 6 | EK_MID | BB | Mid channel | |||
| 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | EK_OFF | BB | Offshore | |||
| 1 | 5 | 6 | 5 | 6 | 4 | 4 | 5 | UK_IN | UK | Inshore | |||
| 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | UK_MID | UK | Mid channel | |||
| 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | UK_OFF | UK | Offshore | |||
| 5 | 6 | 5 | 7 | 5 | 4 | 6 | MR | UK | Offshore | ||||
| 5 | 6 | 5 | 7 | 5 | 4 | 6 | WS | LK | Offshore |
Remove extreme events?
# Label and filter extreme events
FLK.data$Extreme<-as.factor(FLK.data$Extreme)
summary(FLK.data$Extreme)## ColdMortality HighOmega_NoReason Low_pH LowSalinty
## 24 28 110 28
## Normal
## 1384 FLK.data_filtered<-FLK.data[FLK.data$Extreme=="Normal",]3. TA and DIC salinity normalization
Salinity standardizations
- Uses salinity Bottle first, if not available, uses salinity from CTD
#MeanSalinity<-mean(FLK.data[FLK.data$Zone=="Oceanic",]$BestSalinity)
MeanSalinity<-mean(FLK.data$BestSalinity)
sdSalinity<-sd(FLK.data$BestSalinity)
Sal<-paste("Mean salinity = ", round(MeanSalinity, digits=3),
"(", round(sdSalinity, digits=3), "sd)")
Sal## [1] "Mean salinity = 35.992 ( 0.839 sd)"TA_Sal<- ggplot(FLK.data) + MyTheme+
geom_point(aes (BestSalinity, TA_umol_kg, fill=Season),
alpha=0.5, shape=21, size=1)+
geom_smooth(aes(BestSalinity, TA_umol_kg),
linetype=2, method = "lm", color="black")
ggExtra::ggMarginal(
p = TA_Sal,
type = 'boxplot',
margins = 'both',
size = 5,
colour = 'black',
fill = 'gray'
)
TA_Sal + facet_wrap(~Zone)
TA_Sal+ facet_grid(Zone~Season)
#TA_Sal+ facet_grid(Zone~Precipitation)
DIC_Sal<- ggplot(FLK.data) + MyTheme+
geom_point(aes (BestSalinity, DIC_umol_kg, fill=Season),
alpha=0.5, shape=21, size=1)+
geom_smooth(aes(BestSalinity, DIC_umol_kg),
linetype=2, method = "lm", color="black")
ggExtra::ggMarginal(
p = DIC_Sal,
type = 'boxplot',
margins = 'both',
size = 5,
colour = 'black',
fill = 'gray'
)
DIC_Sal + facet_wrap(~Zone)
DIC_Sal+ facet_grid(Zone~Season)
- Calculate TA intercepts
Note: Use Model II Linear regressions!
“For Model II regressions, neither X nor Y is an INDEPENDENT variable but both are assumed to be DEPENDENT on some other parameter which is often unknown. Neither are”controlled”, both are measured, and both include some error. We do not seek an equation of how Y varies in response to a change in X, but rather we look for how they both co-vary in time or space in response to some other variable or process.”
- Use ordinary least squares (OLS)?
All data
##
## Model II regression
##
## Call: lmodel2(formula = TA_umol_kg ~ BestSalinity, data =
## FLK.data_filtered)
##
## n = 1381 r = -0.2393467 r-square = 0.05728686
## Parametric P-values: 2-tailed = 1.917474e-19 1-tailed = 9.587369e-20
## Angle between the two OLS regression lines = 2.607785 degrees
##
## Regression results
## Method Intercept Slope Angle (degrees) P-perm (1-tailed)
## 1 OLS 3109.940 -20.69533 -87.23361 NA
## 2 MA 15397.963 -361.21227 -89.84138 NA
## 3 SMA 5483.361 -86.46588 -89.33739 NA
##
## Confidence intervals
## Method 2.5%-Intercept 97.5%-Intercept 2.5%-Slope 97.5%-Slope
## 1 OLS 2949.866 3270.013 -25.13021 -16.26045
## 2 MA 13097.622 18953.084 -459.72925 -297.46686
## 3 SMA 5327.424 5647.501 -91.01442 -82.14466
##
## Eigenvalues: 4200.394 0.529631
##
## H statistic used for computing C.I. of MA: 3.519553e-07
Oceanic
##
## Model II regression
##
## Call: lmodel2(formula = TA_umol_kg ~ BestSalinity, data =
## FLK.data_filtered[FLK.data_filtered$Zone == "Oceanic", ])
##
## n = 178 r = 0.3189466 r-square = 0.101727
## Parametric P-values: 2-tailed = 1.430211e-05 1-tailed = 7.151053e-06
## Angle between the two OLS regression lines = 7.041072 degrees
##
## Regression results
## Method Intercept Slope Angle (degrees) P-perm (1-tailed)
## 1 OLS 2118.7059 7.258729 82.15601 NA
## 2 MA -186.4667 71.231288 89.19569 NA
## 3 SMA 1560.1927 22.758442 87.48406 NA
##
## Confidence intervals
## Method 2.5%-Intercept 97.5%-Intercept 2.5%-Slope 97.5%-Slope
## 1 OLS 2003.070 2234.3417 4.049985 10.46747
## 2 MA -2219.804 600.4083 49.394133 127.65994
## 3 SMA 1436.459 1667.7051 19.774788 26.19228
##
## Eigenvalues: 147.9711 0.2565248
##
## H statistic used for computing C.I. of MA: 3.849783e-05
- Calculate DIC intercepts
All data
##
## Model II regression
##
## Call: lmodel2(formula = DIC_umol_kg ~ BestSalinity, data =
## FLK.data_filtered)
##
## n = 1373 r = -0.3754391 r-square = 0.1409545
## Parametric P-values: 2-tailed = 3.349539e-47 1-tailed = 1.67477e-47
## Angle between the two OLS regression lines = 1.390794 degrees
##
## Regression results
## Method Intercept Slope Angle (degrees) P-perm (1-tailed)
## 1 OLS 3309.515 -35.37868 -88.38093 NA
## 2 MA 11089.152 -250.96938 -89.77170 NA
## 3 SMA 5433.280 -94.23282 -89.39200 NA
##
## Confidence intervals
## Method 2.5%-Intercept 97.5%-Intercept 2.5%-Slope 97.5%-Slope
## 1 OLS 3142.504 3476.527 -40.00593 -30.75143
## 2 MA 10041.663 12451.874 -288.73336 -221.94116
## 3 SMA 5270.401 5604.352 -98.97362 -89.71911
##
## Eigenvalues: 5011.512 0.4848046
##
## H statistic used for computing C.I. of MA: 2.715863e-07
Oceanic
##
## Model II regression
##
## Call: lmodel2(formula = DIC_umol_kg ~ BestSalinity, data =
## FLK.data_filtered[FLK.data_filtered$Zone == "Oceanic", ])
##
## n = 176 r = 0.2408428 r-square = 0.05800523
## Parametric P-values: 2-tailed = 0.001282336 1-tailed = 0.0006411678
## Angle between the two OLS regression lines = 5.386842 degrees
##
## Regression results
## Method Intercept Slope Angle (degrees) P-perm (1-tailed)
## 1 OLS 1686.0115 9.983939 84.28028 NA
## 2 MA -4152.7476 172.027010 89.66694 NA
## 3 SMA 552.0712 41.454182 88.61812 NA
##
## Confidence intervals
## Method 2.5%-Intercept 97.5%-Intercept 2.5%-Slope 97.5%-Slope
## 1 OLS 1469.0737 1902.9494 3.963929 16.00395
## 2 MA -13566.0607 -1821.0943 107.316641 433.27467
## 3 SMA 319.4893 753.3171 35.869007 47.90903
##
## Eigenvalues: 496.057 0.271903
##
## H statistic used for computing C.I. of MA: 1.22848e-05
- Add TA and DIC endmembers (S=0) to data frame
# 4. Add endmember intercepts
TA_SO<-subset(TA_coef, term=="(Intercept)")
TA_SO<-as.data.frame(dplyr::select(TA_SO, c(Region, estimate)))
names(TA_SO)[2] <- "TA_0"
DIC_SO<-subset(DIC_coef, term=="(Intercept)")
DIC_SO<-as.data.frame(dplyr::select(DIC_SO, c(Region, estimate)))
names(DIC_SO)[2] <- "DIC_0"
Intercepts<-join(TA_SO, DIC_SO, by="Region", type="full")
FLK.data<-join(FLK.data, Intercepts, by="Region", type="left")- Normalize with (1) Friss equation and (2) mean salinity
# 5. endmember and salinity (mean) normalization
FLK.data$nTA<-((
(FLK.data$TA_umol_kg-FLK.data$TA_0)/
FLK.data$BestSalinity)*MeanSalinity)+FLK.data$TA_0
FLK.data$nDIC<-((
(FLK.data$DIC_umol_kg-FLK.data$DIC_0)/
FLK.data$BestSalinity)*MeanSalinity)+FLK.data$DIC_0
# 2. TA and DIC 35 normalization
FLK.data$n35TA<-(FLK.data$TA_umol_kg/FLK.data$BestSalinity)*MeanSalinity
FLK.data$n35DIC<-(FLK.data$DIC_umol_kg/FLK.data$BestSalinity)*MeanSalinity
# FLK.data$n35TA<-(FLK.data$TA_umol_kg/FLK.data$BestSalinity)*35
# FLK.data$n35DIC<-(FLK.data$DIC_umol_kg/FLK.data$BestSalinity)*35
#write.csv(FLK.data, "FLK_results/FLK.data_filtered_normalized.csv", row.names = F)- TA parameters to nTA (S=0)
kable(as.data.frame(TA_coef[,1:8], format = "markdown"),
caption = "Table 1: TA parameters for salinity normalization with endmember S=0",
digits = 3)%>%
kable_styling(bootstrap_options = c("striped", "condensed", full_width = F), font_size = 11)| Region | term | estimate | std.error | statistic | p.value | r.squared | adj.r.squared |
|---|---|---|---|---|---|---|---|
| FL | (Intercept) | 2118.706 | 58.593 | 36.160 | 0 | 0.102 | 0.097 |
| FL | BestSalinity | 7.259 | 1.626 | 4.464 | 0 | 0.102 | 0.097 |
#write.csv(TA_coef, "TA_coef.csv")# Plot
Normalization_plot<- ggplot(FLK.data) +
theme_bw() +
theme(legend.position="bottom",
plot.background=element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
legend.box.background = element_rect(),
legend.title = element_blank(),
panel.background =element_rect(fill = NA,
color = "black"))+
scale_x_continuous(limits = c(31.5, 39),
expand = c(0.05, 0.05),
breaks = seq(30, 40, 2),
name=expression(paste("Salinity")))- DIC parameters to nDIC (S=0)
kable(as.data.frame(DIC_coef[,1:8], format = "markdown"),
caption = "Table 2: DIC parameters for salinity normalization with endmember S0",
digits = 2) %>%
kable_styling(bootstrap_options = c("striped", "condensed", full_width = F), font_size = 11)| Region | term | estimate | std.error | statistic | p.value | r.squared | adj.r.squared |
|---|---|---|---|---|---|---|---|
| FL | (Intercept) | 1686.01 | 109.91 | 15.34 | 0 | 0.06 | 0.05 |
| FL | BestSalinity | 9.98 | 3.05 | 3.27 | 0 | 0.06 | 0.05 |
#write.csv(DIC_coef, "DIC_coef.csv")
Figure Supp 1: Overview of linear regressions of total alkalinity (TA, µmol kg−1) and dissolved inorganic carbon (DIC, µmol kg−1) as a function of salinity + Friss normalized values.
- Original data: Black line and circles
- Friis normalization S0 end-member: purple line and pointing up triangles
- Mean salinity normalization: purple line and pointing down triangles
TA vs Friss salinity normalized TA
Figure Supp X: Linear regressions of Friss normalized total alkalinity (nTA, µmol kg−1) as a function of measured Total Alcalinity (TA, µmol kg−1)
DIC vs Friss salinity normalized DIC
Figure Supp X: Linear regressions of Friss normalized Dissolved Inorganc Carbon (nDIC, µmol kg−1) as a function of measured Dissolved Inorganc Carbon (DIC, µmol kg−1)
TA vs Salinity normalized TA
Figure Supp X: Linear regressions of mean salinity normalized total alkalinity (n35TA, µmol kg−1) as a function of measured Total Alcalinity (TA, µmol kg−1)
DIC vs Salinity normalized DIC
Figure Supp X: Linear regressions of mean salinity normalized Dissolved Inorganc Carbon (n35DIC, µmol kg−1) as a function of measured Dissolved Inorganc Carbon (DIC, µmol kg−1)
Differences vs Salinity
Figure Supp X: Linear regressions of (A) TA-n35TA and (B) DIC-nDIC as a function of Salinity
4. Endmember comparissions
FLK.data_filtered<-FLK.data[FLK.data$Extreme=="Normal",]4.1 TA and DIC relation
No_ocean.data<-FLK.data[FLK.data$Zone!="Oceanic",]
TA_DIC<- ggplot(No_ocean.data, aes (DIC_umol_kg, TA_umol_kg)) +
scale_y_continuous(#limits = c(1700,2900),
#expand = c(0, 0),
#breaks = seq(1800, 2800, 100),
expression(paste("TA (", mu, "mol ", kg^-1, ")")))+
scale_x_continuous(# limits = c(1600, 2600),
# expand = c(0, 0),
# breaks = seq(1500,2600,100),
expression(paste("DIC (", mu, "mol ", kg^-1, ")")))+
facet_grid(Sub_region~Zone)+ MyTheme+
theme(legend.position = "bottom")+
#Season_fill+ Season_colour+
guides(fill=guide_legend(override.aes=list(shape=21)))TA_DIC_Zone <- ddply (FLK.data[FLK.data$Extreme=="Normal", ], .(Zone),summarise,
TAmean = mean (TA_umol_kg, na.rm = T),
DICmean = mean (DIC_umol_kg, na.rm = T),
nTAmean = mean (n35TA, na.rm = T),
nDICmean = mean (n35DIC, na.rm = T),
TAsd = sd (TA_umol_kg, na.rm = T),
DICsd = sd (DIC_umol_kg, na.rm = T),
nTAsd = sd (n35TA, na.rm = T),
nDIsd = sd (n35DIC, na.rm = T))
TA_DIC_Ocean<-TA_DIC_Zone[TA_DIC_Zone$Zone=="Oceanic", ]
kable(as.data.frame(TA_DIC_Zone, format = "markdown"),
caption = "Mean TA/DIC by zone") %>%
kable_styling(bootstrap_options = c("striped", "condensed", full_width = F), font_size = 11)| Zone | TAmean | DICmean | nTAmean | nDICmean | TAsd | DICsd | nTAsd | nDIsd |
|---|---|---|---|---|---|---|---|---|
| Inshore | 2339.763 | 2015.378 | 2332.157 | 2009.834 | 102.84315 | 113.22345 | 140.78447 | 151.31755 |
| Mid channel | 2364.838 | 2032.799 | 2360.998 | 2029.781 | 52.39024 | 58.11765 | 79.54278 | 82.51523 |
| Offshore | 2376.329 | 2043.844 | 2372.391 | 2040.360 | 19.67731 | 23.76709 | 37.86066 | 36.46979 |
| Oceanic | 2380.265 | 2045.754 | 2378.003 | 2043.875 | 12.16314 | 22.27196 | 34.34641 | 33.66538 |
Note: Use Model II Linear regressions!
“For Model II regressions, neither X nor Y is an INDEPENDENT variable but both are assumed to be DEPENDENT on some other parameter which is often unknown. Neither are”controlled”, both are measured, and both include some error. We do not seek an equation of how Y varies in response to a change in X, but rather we look for how they both co-vary in time or space in response to some other variable or process.”
Zone and region
# Individual LR for each Zone and Sub_region
TA_DIC_coef_zone_noExtremes <- No_ocean.data[No_ocean.data$Extreme=="Normal", ] %>%
group_by(Zone, Sub_region) %>%
do({model = lm(TA_umol_kg~DIC_umol_kg, data=.)
# create model
data.frame(tidy(model), # get coefficient info
glance(model))}) # get model info
TA_DIC_coef_zone_noExtremes<-subset(TA_DIC_coef_zone_noExtremes, term!="(Intercept)")
kable(as.data.frame(TA_DIC_coef_zone_noExtremes[,1:8],
format = "markdown"),
caption = "TA vs DIC equations by zone and region without extreme events",
digits = 2)%>%
kable_styling(bootstrap_options = c("striped", "condensed", full_width = F), font_size = 11)| Zone | Sub_region | term | estimate | std.error | statistic | p.value | r.squared |
|---|---|---|---|---|---|---|---|
| Inshore | BB | DIC_umol_kg | 0.67 | 0.04 | 15.22 | 0 | 0.76 |
| Inshore | UK | DIC_umol_kg | 0.58 | 0.03 | 18.50 | 0 | 0.78 |
| Inshore | MK | DIC_umol_kg | 0.84 | 0.03 | 24.79 | 0 | 0.85 |
| Inshore | LK | DIC_umol_kg | 0.86 | 0.04 | 20.28 | 0 | 0.78 |
| Mid channel | BB | DIC_umol_kg | 0.57 | 0.06 | 9.71 | 0 | 0.58 |
| Mid channel | UK | DIC_umol_kg | 0.41 | 0.07 | 6.04 | 0 | 0.37 |
| Mid channel | MK | DIC_umol_kg | 0.83 | 0.04 | 21.55 | 0 | 0.81 |
| Mid channel | LK | DIC_umol_kg | 0.81 | 0.04 | 20.72 | 0 | 0.79 |
| Offshore | BB | DIC_umol_kg | 0.58 | 0.09 | 6.70 | 0 | 0.38 |
| Offshore | UK | DIC_umol_kg | 0.57 | 0.05 | 10.84 | 0 | 0.55 |
| Offshore | MK | DIC_umol_kg | 0.77 | 0.04 | 19.96 | 0 | 0.78 |
| Offshore | LK | DIC_umol_kg | 0.57 | 0.04 | 13.61 | 0 | 0.56 |
#write.csv(TA_DIC_coef_zone_noExtremes, "FLK_results/Fig_2_TA_DIC_coef_slopes_Subregion.csv", row.names = FALSE)
# Annotations for plots
coef_zone_noExtremes<-dplyr::select(TA_DIC_coef_zone_noExtremes,
c("Zone", "Sub_region", "estimate", "r.squared"))
coef_zone_noExtremes$estimate<-round(coef_zone_noExtremes$estimate, digits=2)
coef_zone_noExtremes$r.squared<-round(coef_zone_noExtremes$r.squared, digits=2)Type II Linear regression
TA_DIC_coef_zone_noExtII <- No_ocean.data[No_ocean.data$Extreme=="Normal", ] %>%
group_by(Zone, Sub_region) %>%
do({model = lmodel2(TA_umol_kg~DIC_umol_kg, data=.)
# create model
data.frame(tidy(model), # get coefficient info
glance(model))}) # get model info
#TA_DIC_coef_zone_noExtII
TA_DIC_coef_zone_noExtII<-subset(TA_DIC_coef_zone_noExtII, term!="Intercept")
kable(as.data.frame(TA_DIC_coef_zone_noExtII[,1:9],
format = "markdown"),
caption = "TA vs DIC equations without extreme events using type II lm",
digits = 2)%>%
kable_styling(bootstrap_options = c("striped", "condensed", full_width = F), font_size = 11)| Zone | Sub_region | method | term | estimate | conf.low | conf.high | p.value | r.squared |
|---|---|---|---|---|---|---|---|---|
| Inshore | BB | MA | Slope | 0.74 | 0.64 | 0.84 | 0.76 | |
| Inshore | BB | OLS | Slope | 0.67 | 0.58 | 0.76 | 0.76 | |
| Inshore | BB | SMA | Slope | 0.77 | 0.68 | 0.86 | 0.76 | |
| Inshore | UK | MA | Slope | 0.62 | 0.56 | 0.69 | 0.78 | |
| Inshore | UK | OLS | Slope | 0.58 | 0.52 | 0.64 | 0.78 | |
| Inshore | UK | SMA | Slope | 0.66 | 0.60 | 0.72 | 0.78 | |
| Inshore | MK | MA | Slope | 0.91 | 0.84 | 0.98 | 0.85 | |
| Inshore | MK | OLS | Slope | 0.84 | 0.77 | 0.91 | 0.85 | |
| Inshore | MK | SMA | Slope | 0.91 | 0.85 | 0.98 | 0.85 | |
| Inshore | LK | MA | Slope | 0.97 | 0.88 | 1.06 | 0.78 | |
| Inshore | LK | OLS | Slope | 0.86 | 0.77 | 0.94 | 0.78 | |
| Inshore | LK | SMA | Slope | 0.97 | 0.89 | 1.06 | 0.78 | |
| Mid channel | BB | MA | Slope | 0.69 | 0.55 | 0.84 | 0.58 | |
| Mid channel | BB | OLS | Slope | 0.57 | 0.45 | 0.69 | 0.58 | |
| Mid channel | BB | SMA | Slope | 0.75 | 0.64 | 0.87 | 0.58 | |
| Mid channel | UK | MA | Slope | 0.54 | 0.37 | 0.73 | 0.37 | |
| Mid channel | UK | OLS | Slope | 0.41 | 0.28 | 0.55 | 0.37 | |
| Mid channel | UK | SMA | Slope | 0.68 | 0.55 | 0.83 | 0.37 | |
| Mid channel | MK | MA | Slope | 0.91 | 0.83 | 1.00 | 0.81 | |
| Mid channel | MK | OLS | Slope | 0.83 | 0.75 | 0.90 | 0.81 | |
| Mid channel | MK | SMA | Slope | 0.92 | 0.85 | 1.00 | 0.81 | |
| Mid channel | LK | MA | Slope | 0.90 | 0.82 | 0.99 | 0.79 | |
| Mid channel | LK | OLS | Slope | 0.81 | 0.73 | 0.88 | 0.79 | |
| Mid channel | LK | SMA | Slope | 0.91 | 0.84 | 0.99 | 0.79 | |
| Offshore | BB | MA | Slope | 0.89 | 0.65 | 1.20 | 0.38 | |
| Offshore | BB | OLS | Slope | 0.58 | 0.40 | 0.75 | 0.38 | |
| Offshore | BB | SMA | Slope | 0.93 | 0.77 | 1.12 | 0.38 | |
| Offshore | UK | MA | Slope | 0.70 | 0.58 | 0.84 | 0.55 | |
| Offshore | UK | OLS | Slope | 0.57 | 0.47 | 0.67 | 0.55 | |
| Offshore | UK | SMA | Slope | 0.77 | 0.67 | 0.88 | 0.55 | |
| Offshore | MK | MA | Slope | 0.86 | 0.78 | 0.95 | 0.78 | |
| Offshore | MK | OLS | Slope | 0.77 | 0.70 | 0.85 | 0.78 | |
| Offshore | MK | SMA | Slope | 0.87 | 0.80 | 0.95 | 0.78 | |
| Offshore | LK | MA | Slope | 0.70 | 0.60 | 0.80 | 0.56 | |
| Offshore | LK | OLS | Slope | 0.57 | 0.49 | 0.65 | 0.56 | |
| Offshore | LK | SMA | Slope | 0.76 | 0.68 | 0.85 | 0.56 |
# Annotations for plots
TA_DIC_coef_zone_noExtII<-dplyr::select(TA_DIC_coef_zone_noExtII,
c("Zone", "Sub_region","method", "estimate", "r.squared"))
TA_DIC_coef_zone_noExtII$estimate<-round(TA_DIC_coef_zone_noExtII$estimate, digits=2)
TA_DIC_coef_zone_noExtII$r.squared<-round(TA_DIC_coef_zone_noExtII$r.squared, digits=2)4.2 By Precipitation - Removing Extreme events
TA_DIC_Extreme_pre<-TA_DIC +
geom_abline(intercept = -1700, slope=2, color="grey")+
geom_hline (yintercept = TA_DIC_Ocean$TAmean, color="grey")+
scale_shape_manual(values=c(24, 25, 23, 22, 21))+
geom_point(data=No_ocean.data, aes (DIC_umol_kg, TA_umol_kg,
fill=Precipitation, shape=Extreme),
alpha=0.5, size=1)+
geom_smooth(data=No_ocean.data[No_ocean.data$Extreme=="Normal", ],
aes(DIC_umol_kg, TA_umol_kg),
method = "lm", colour="black", se=F)+
geom_smooth(data=No_ocean.data[No_ocean.data$Extreme=="Normal", ],
aes(DIC_umol_kg, TA_umol_kg, colour=Precipitation),
method = "lm", se=F, size=0.5)+
geom_text(data = coef_zone_noExtremes,
aes(x = TA_DIC_Ocean$DICmean, y = 2070,
label = paste("Slo =", estimate,
" r.sq=", r.squared)),
size=2.5)+
# geom_abline(slope = 0, intercept =TA_DIC_Ocean$TAmean,
# linetype=2, colour="gray")+
# geom_vline(xintercept = TA_DIC_Ocean$DICmean,
# linetype=2, colour="gray")+
geom_point(aes(x=TA_DIC_Ocean$DICmean, y=TA_DIC_Ocean$TAmean),
size=3, alpha=1, shape=21, fill="black")
#TA_DIC_Extreme_preZone, region and transect
TA_DIC_reef_pre<-TA_DIC_Extreme_pre +
facet_grid(Zone~Reference)
#TA_DIC_reef_pre# Individual LR for each reef
TA_DIC_coef_Reef_noExtremes <- No_ocean.data[No_ocean.data$Extreme=="Normal", ] %>%
group_by(Zone, Sub_region, Reference, Precipitation) %>%
do({model = lm(TA_umol_kg~DIC_umol_kg, data=.)
# create model
data.frame(tidy(model), # get coefficient info
glance(model))}) # get model info
TA_DIC_coef_Reef_noExtremes<-subset(TA_DIC_coef_Reef_noExtremes, term!="(Intercept)")
kable(as.data.frame(TA_DIC_coef_Reef_noExtremes[,1:11],
format = "markdown"),
caption = "TA vs DIC equations by reef without extreme events per trsansec",
digits = 2)%>%
kable_styling(bootstrap_options = c("striped", "condensed", full_width = F), font_size = 11)| Zone | Sub_region | Reference | Precipitation | term | estimate | std.error | statistic | p.value | r.squared | adj.r.squared |
|---|---|---|---|---|---|---|---|---|---|---|
| Inshore | BB | BB_1 | Dry | DIC_umol_kg | 0.58 | 0.05 | 10.57 | 0.00 | 0.85 | 0.85 |
| Inshore | BB | BB_1 | Wet | DIC_umol_kg | 0.72 | 0.07 | 10.43 | 0.00 | 0.86 | 0.86 |
| Inshore | BB | BB_2 | Dry | DIC_umol_kg | 0.54 | 0.07 | 7.89 | 0.00 | 0.79 | 0.77 |
| Inshore | BB | BB_2 | Wet | DIC_umol_kg | 0.47 | 0.15 | 3.15 | 0.01 | 0.42 | 0.37 |
| Inshore | UK | UK_3 | Dry | DIC_umol_kg | 0.58 | 0.03 | 18.79 | 0.00 | 0.92 | 0.91 |
| Inshore | UK | UK_3 | Wet | DIC_umol_kg | 0.47 | 0.09 | 5.16 | 0.00 | 0.50 | 0.48 |
| Inshore | UK | UK_4 | Dry | DIC_umol_kg | 0.54 | 0.05 | 11.46 | 0.00 | 0.90 | 0.89 |
| Inshore | UK | UK_4 | Wet | DIC_umol_kg | 0.35 | 0.22 | 1.59 | 0.13 | 0.15 | 0.09 |
| Inshore | MK | MK_5 | Dry | DIC_umol_kg | 0.76 | 0.07 | 10.67 | 0.00 | 0.86 | 0.86 |
| Inshore | MK | MK_5 | Wet | DIC_umol_kg | 0.79 | 0.10 | 7.75 | 0.00 | 0.78 | 0.77 |
| Inshore | MK | MK_6 | Dry | DIC_umol_kg | 0.72 | 0.09 | 8.38 | 0.00 | 0.81 | 0.80 |
| Inshore | MK | MK_6 | Wet | DIC_umol_kg | 0.80 | 0.13 | 6.25 | 0.00 | 0.70 | 0.68 |
| Inshore | MK | MK_7 | Dry | DIC_umol_kg | 0.67 | 0.08 | 8.84 | 0.00 | 0.84 | 0.83 |
| Inshore | MK | MK_7 | Wet | DIC_umol_kg | 0.93 | 0.15 | 6.18 | 0.00 | 0.69 | 0.67 |
| Inshore | LK | LK_8 | Dry | DIC_umol_kg | 0.79 | 0.10 | 8.00 | 0.00 | 0.78 | 0.77 |
| Inshore | LK | LK_8 | Wet | DIC_umol_kg | 0.80 | 0.11 | 7.18 | 0.00 | 0.76 | 0.75 |
| Inshore | LK | LK_9 | Dry | DIC_umol_kg | 0.68 | 0.09 | 7.62 | 0.00 | 0.77 | 0.76 |
| Inshore | LK | LK_9 | Wet | DIC_umol_kg | 0.55 | 0.19 | 2.85 | 0.01 | 0.32 | 0.28 |
| Inshore | LK | LK_10 | Dry | DIC_umol_kg | 0.84 | 0.14 | 5.87 | 0.00 | 0.63 | 0.61 |
| Inshore | LK | LK_10 | Wet | DIC_umol_kg | 0.64 | 0.14 | 4.74 | 0.00 | 0.57 | 0.54 |
| Mid channel | BB | BB_1 | Dry | DIC_umol_kg | 0.53 | 0.08 | 6.67 | 0.00 | 0.72 | 0.71 |
| Mid channel | BB | BB_1 | Wet | DIC_umol_kg | 0.60 | 0.14 | 4.36 | 0.00 | 0.56 | 0.53 |
| Mid channel | BB | BB_2 | Dry | DIC_umol_kg | 0.52 | 0.09 | 5.54 | 0.00 | 0.66 | 0.64 |
| Mid channel | BB | BB_2 | Wet | DIC_umol_kg | 0.38 | 0.29 | 1.30 | 0.22 | 0.12 | 0.05 |
| Mid channel | UK | UK_3 | Dry | DIC_umol_kg | 0.36 | 0.14 | 2.58 | 0.02 | 0.36 | 0.30 |
| Mid channel | UK | UK_3 | Wet | DIC_umol_kg | 0.00 | 0.17 | -0.02 | 0.98 | 0.00 | -0.07 |
| Mid channel | UK | UK_4 | Dry | DIC_umol_kg | 0.47 | 0.10 | 4.47 | 0.00 | 0.56 | 0.53 |
| Mid channel | UK | UK_4 | Wet | DIC_umol_kg | 0.63 | 0.25 | 2.57 | 0.02 | 0.32 | 0.27 |
| Mid channel | MK | MK_5 | Dry | DIC_umol_kg | 0.47 | 0.10 | 4.53 | 0.00 | 0.53 | 0.51 |
| Mid channel | MK | MK_5 | Wet | DIC_umol_kg | 0.88 | 0.17 | 5.18 | 0.00 | 0.61 | 0.59 |
| Mid channel | MK | MK_6 | Dry | DIC_umol_kg | 0.59 | 0.09 | 6.35 | 0.00 | 0.70 | 0.69 |
| Mid channel | MK | MK_6 | Wet | DIC_umol_kg | 0.83 | 0.10 | 8.45 | 0.00 | 0.82 | 0.81 |
| Mid channel | MK | MK_7 | Dry | DIC_umol_kg | 0.63 | 0.07 | 8.50 | 0.00 | 0.82 | 0.81 |
| Mid channel | MK | MK_7 | Wet | DIC_umol_kg | 1.09 | 0.12 | 9.25 | 0.00 | 0.84 | 0.83 |
| Mid channel | LK | LK_8 | Dry | DIC_umol_kg | 0.56 | 0.16 | 3.50 | 0.00 | 0.39 | 0.36 |
| Mid channel | LK | LK_8 | Wet | DIC_umol_kg | 0.96 | 0.07 | 14.05 | 0.00 | 0.92 | 0.92 |
| Mid channel | LK | LK_9 | Dry | DIC_umol_kg | 0.67 | 0.10 | 6.51 | 0.00 | 0.70 | 0.69 |
| Mid channel | LK | LK_9 | Wet | DIC_umol_kg | 0.92 | 0.12 | 7.51 | 0.00 | 0.77 | 0.75 |
| Mid channel | LK | LK_10 | Dry | DIC_umol_kg | 0.42 | 0.18 | 2.37 | 0.03 | 0.22 | 0.18 |
| Mid channel | LK | LK_10 | Wet | DIC_umol_kg | 0.88 | 0.13 | 6.51 | 0.00 | 0.73 | 0.71 |
| Offshore | BB | BB_1 | Dry | DIC_umol_kg | 0.53 | 0.11 | 4.70 | 0.00 | 0.54 | 0.51 |
| Offshore | BB | BB_1 | Wet | DIC_umol_kg | 0.61 | 0.22 | 2.82 | 0.01 | 0.32 | 0.28 |
| Offshore | BB | BB_2 | Dry | DIC_umol_kg | 0.49 | 0.15 | 3.28 | 0.00 | 0.40 | 0.37 |
| Offshore | BB | BB_2 | Wet | DIC_umol_kg | 0.57 | 0.31 | 1.82 | 0.09 | 0.19 | 0.13 |
| Offshore | UK | UK_3 | Dry | DIC_umol_kg | 0.63 | 0.10 | 6.55 | 0.00 | 0.60 | 0.58 |
| Offshore | UK | UK_3 | Wet | DIC_umol_kg | 0.56 | 0.15 | 3.82 | 0.00 | 0.32 | 0.30 |
| Offshore | UK | UK_4 | Dry | DIC_umol_kg | 0.48 | 0.09 | 5.19 | 0.00 | 0.63 | 0.60 |
| Offshore | UK | UK_4 | Wet | DIC_umol_kg | 0.90 | 0.13 | 6.90 | 0.00 | 0.77 | 0.76 |
| Offshore | MK | MK_5 | Dry | DIC_umol_kg | 0.50 | 0.10 | 5.09 | 0.00 | 0.59 | 0.57 |
| Offshore | MK | MK_5 | Wet | DIC_umol_kg | 0.85 | 0.07 | 12.02 | 0.00 | 0.89 | 0.89 |
| Offshore | MK | MK_6 | Dry | DIC_umol_kg | 0.51 | 0.10 | 4.89 | 0.00 | 0.56 | 0.53 |
| Offshore | MK | MK_6 | Wet | DIC_umol_kg | 1.07 | 0.11 | 9.62 | 0.00 | 0.86 | 0.85 |
| Offshore | MK | MK_7 | Dry | DIC_umol_kg | 0.43 | 0.09 | 4.67 | 0.00 | 0.58 | 0.55 |
| Offshore | MK | MK_7 | Wet | DIC_umol_kg | 0.95 | 0.10 | 9.22 | 0.00 | 0.84 | 0.83 |
| Offshore | LK | LK_8 | Dry | DIC_umol_kg | 0.25 | 0.08 | 3.09 | 0.01 | 0.35 | 0.31 |
| Offshore | LK | LK_8 | Wet | DIC_umol_kg | 0.97 | 0.11 | 8.46 | 0.00 | 0.80 | 0.79 |
| Offshore | LK | LK_9 | Dry | DIC_umol_kg | 0.55 | 0.09 | 6.41 | 0.00 | 0.73 | 0.71 |
| Offshore | LK | LK_9 | Wet | DIC_umol_kg | 0.83 | 0.12 | 6.98 | 0.00 | 0.75 | 0.74 |
| Offshore | LK | LK_10 | Dry | DIC_umol_kg | 0.48 | 0.10 | 4.91 | 0.00 | 0.41 | 0.40 |
| Offshore | LK | LK_10 | Wet | DIC_umol_kg | 0.77 | 0.13 | 5.72 | 0.00 | 0.50 | 0.48 |
#write.csv (TA_DIC_coef_Reef_noExtremes, "FLK_results/Fig_2_TA_DIC_slopes_Reefs.csv", row.names = FALSE)
TA_DIC_coef_Reef_noExtremes <-
No_ocean.data[No_ocean.data$Extreme=="Normal", ] %>%
group_by(Zone, Region, Sub_region, Transect, Precipitation) %>%
do({model = lm(TA_umol_kg~DIC_umol_kg, data=.)
# create model
data.frame(tidy(model), # get coefficient info
glance(model))}) # get model infoSlopes_reef<-subset(TA_DIC_coef_Reef_noExtremes, term!="(Intercept)")
Slope_plot<-ggplot(data=Slopes_reef, aes (x =Zone, y =estimate)) +
MyTheme+
scale_shape_manual(values=c(24, 25, 23, 22, 21))+
geom_boxplot(aes(fill=(Precipitation)), alpha=0.5,
position=position_dodge(width=0.3))+
geom_point(aes(fill = factor(Precipitation)),
position=position_dodge(width=0.3),
shape = 21, size=0.5)+
scale_y_continuous(#limits = c(1700,2900),
#expand = c(0, 0),
#breaks = seq(1800, 2800, 100),
expression(paste("TA vs DIC slope")))+
theme(axis.text.x = element_text(angle = 0, vjust = 0.1),
legend.position = "bottom")+
#guides(fill=FALSE)+
facet_grid(Sub_region~Region)
# Season_fill+ Season_colour+
# theme(legend.title = element_blank())
#Slope_plot
Figure S9: TA (µmol kg−1) vs DIC (µmol kg−1) slopes per season and reef
4.3 By Season - Removing Extreme events
TA_DIC_season<-TA_DIC +
Season_colour+ Season_fill+
geom_abline(intercept = -1700, slope=2, color="grey")+
geom_hline (yintercept = TA_DIC_Ocean$TAmean, color="grey")+
scale_shape_manual(values=c(24, 25, 23, 22, 21))+
geom_point(data=No_ocean.data, aes (DIC_umol_kg, TA_umol_kg,
fill=Season, shape=Extreme),
alpha=0.5, size=1)+
geom_smooth(data=No_ocean.data[No_ocean.data$Extreme=="Normal", ],
aes(DIC_umol_kg, TA_umol_kg),
method = "lm", colour="black", se=F)+
geom_smooth(data=No_ocean.data[No_ocean.data$Extreme=="Normal", ],
aes(DIC_umol_kg, TA_umol_kg, colour=Season),
method = "lm", se=F, size=0.5)+
geom_text(data = coef_zone_noExtremes,
aes(x = TA_DIC_Ocean$DICmean, y = 2070,
label = paste("Slo =", estimate,
" r.sq=", r.squared)),
size=2.5)+
geom_point(aes(x=TA_DIC_Ocean$DICmean, y=TA_DIC_Ocean$TAmean),
size=3, alpha=1, shape=21, fill="black")
#TA_DIC_season# Individual LR for each reef
TA_DIC_coef_Season_noExtremes <- FLK.data[FLK.data$Extreme=="Normal", ] %>%
group_by(Zone, Sub_region, Season) %>%
do({model = lm(TA_umol_kg~DIC_umol_kg, data=.)
# create model
data.frame(tidy(model), # get coefficient info
glance(model))}) # get model info
TA_DIC_coef_Season_noExtremes<-subset(TA_DIC_coef_Season_noExtremes, term!="(Intercept)")
kable(as.data.frame(TA_DIC_coef_Season_noExtremes[,1:11],
format = "markdown"),
caption = "TA vs DIC equations by season without extreme events",
digits = 2)%>%
kable_styling(bootstrap_options = c("striped", "condensed", full_width = F), font_size = 11)| Zone | Sub_region | Season | term | estimate | std.error | statistic | p.value | r.squared | adj.r.squared | sigma |
|---|---|---|---|---|---|---|---|---|---|---|
| Inshore | BB | Winter | DIC_umol_kg | 0.67 | 0.10 | 6.59 | 0.00 | 0.74 | 0.73 | 23.70 |
| Inshore | BB | Spring | DIC_umol_kg | 0.86 | 0.11 | 7.52 | 0.00 | 0.75 | 0.74 | 36.03 |
| Inshore | BB | Summer | DIC_umol_kg | 0.54 | 0.15 | 3.61 | 0.00 | 0.48 | 0.45 | 37.42 |
| Inshore | BB | Fall | DIC_umol_kg | 0.80 | 0.09 | 8.72 | 0.00 | 0.80 | 0.79 | 22.56 |
| Inshore | UK | Winter | DIC_umol_kg | 0.71 | 0.05 | 13.83 | 0.00 | 0.91 | 0.90 | 21.12 |
| Inshore | UK | Spring | DIC_umol_kg | 0.58 | 0.06 | 8.96 | 0.00 | 0.76 | 0.75 | 22.93 |
| Inshore | UK | Summer | DIC_umol_kg | 0.80 | 0.27 | 2.92 | 0.01 | 0.28 | 0.25 | 42.64 |
| Inshore | UK | Fall | DIC_umol_kg | 0.72 | 0.06 | 11.23 | 0.00 | 0.85 | 0.84 | 27.46 |
| Inshore | MK | Winter | DIC_umol_kg | 0.92 | 0.07 | 13.52 | 0.00 | 0.90 | 0.89 | 35.42 |
| Inshore | MK | Spring | DIC_umol_kg | 0.84 | 0.10 | 8.32 | 0.00 | 0.68 | 0.67 | 41.83 |
| Inshore | MK | Summer | DIC_umol_kg | 1.05 | 0.09 | 12.00 | 0.00 | 0.87 | 0.86 | 37.22 |
| Inshore | MK | Fall | DIC_umol_kg | 0.82 | 0.07 | 11.50 | 0.00 | 0.83 | 0.82 | 27.51 |
| Inshore | LK | Winter | DIC_umol_kg | 0.64 | 0.12 | 5.22 | 0.00 | 0.51 | 0.49 | 44.09 |
| Inshore | LK | Spring | DIC_umol_kg | 0.87 | 0.08 | 10.52 | 0.00 | 0.77 | 0.76 | 35.49 |
| Inshore | LK | Summer | DIC_umol_kg | 0.80 | 0.14 | 5.88 | 0.00 | 0.65 | 0.63 | 47.50 |
| Inshore | LK | Fall | DIC_umol_kg | 1.08 | 0.06 | 17.96 | 0.00 | 0.91 | 0.91 | 25.26 |
| Mid channel | BB | Winter | DIC_umol_kg | 0.80 | 0.12 | 6.54 | 0.00 | 0.75 | 0.74 | 9.85 |
| Mid channel | BB | Spring | DIC_umol_kg | 0.64 | 0.08 | 7.88 | 0.00 | 0.80 | 0.78 | 11.95 |
| Mid channel | BB | Summer | DIC_umol_kg | 0.61 | 0.36 | 1.71 | 0.11 | 0.17 | 0.11 | 21.37 |
| Mid channel | BB | Fall | DIC_umol_kg | 0.56 | 0.11 | 5.32 | 0.00 | 0.63 | 0.60 | 11.63 |
| Mid channel | UK | Winter | DIC_umol_kg | 0.66 | 0.09 | 7.62 | 0.00 | 0.82 | 0.80 | 13.29 |
| Mid channel | UK | Spring | DIC_umol_kg | 0.04 | 0.14 | 0.28 | 0.79 | 0.00 | -0.06 | 14.59 |
| Mid channel | UK | Summer | DIC_umol_kg | 1.09 | 0.32 | 3.45 | 0.00 | 0.50 | 0.46 | 17.14 |
| Mid channel | UK | Fall | DIC_umol_kg | 0.57 | 0.13 | 4.44 | 0.00 | 0.57 | 0.54 | 14.12 |
| Mid channel | MK | Winter | DIC_umol_kg | 0.72 | 0.07 | 10.08 | 0.00 | 0.82 | 0.81 | 21.50 |
| Mid channel | MK | Spring | DIC_umol_kg | 0.97 | 0.08 | 11.47 | 0.00 | 0.81 | 0.80 | 29.81 |
| Mid channel | MK | Summer | DIC_umol_kg | 1.25 | 0.10 | 12.61 | 0.00 | 0.88 | 0.87 | 21.92 |
| Mid channel | MK | Fall | DIC_umol_kg | 0.63 | 0.06 | 10.51 | 0.00 | 0.80 | 0.79 | 14.44 |
| Mid channel | LK | Winter | DIC_umol_kg | 0.73 | 0.14 | 5.10 | 0.00 | 0.48 | 0.46 | 35.75 |
| Mid channel | LK | Spring | DIC_umol_kg | 0.74 | 0.08 | 9.40 | 0.00 | 0.73 | 0.73 | 27.01 |
| Mid channel | LK | Summer | DIC_umol_kg | 0.97 | 0.06 | 15.11 | 0.00 | 0.92 | 0.92 | 18.73 |
| Mid channel | LK | Fall | DIC_umol_kg | 0.82 | 0.16 | 4.96 | 0.00 | 0.44 | 0.42 | 19.50 |
| Offshore | BB | Winter | DIC_umol_kg | 0.74 | 0.12 | 5.98 | 0.00 | 0.70 | 0.68 | 8.49 |
| Offshore | BB | Spring | DIC_umol_kg | 0.48 | 0.13 | 3.63 | 0.00 | 0.41 | 0.38 | 8.38 |
| Offshore | BB | Summer | DIC_umol_kg | 0.84 | 0.37 | 2.27 | 0.04 | 0.27 | 0.22 | 21.24 |
| Offshore | BB | Fall | DIC_umol_kg | 0.32 | 0.11 | 2.93 | 0.01 | 0.32 | 0.29 | 8.97 |
| Offshore | UK | Winter | DIC_umol_kg | 0.63 | 0.08 | 8.01 | 0.00 | 0.75 | 0.74 | 6.58 |
| Offshore | UK | Spring | DIC_umol_kg | 0.48 | 0.17 | 2.86 | 0.01 | 0.25 | 0.22 | 10.70 |
| Offshore | UK | Summer | DIC_umol_kg | 0.93 | 0.14 | 6.78 | 0.00 | 0.70 | 0.68 | 9.77 |
| Offshore | UK | Fall | DIC_umol_kg | 0.48 | 0.11 | 4.36 | 0.00 | 0.44 | 0.42 | 10.84 |
| Offshore | MK | Winter | DIC_umol_kg | 0.62 | 0.09 | 6.95 | 0.00 | 0.68 | 0.66 | 8.56 |
| Offshore | MK | Spring | DIC_umol_kg | 0.89 | 0.07 | 12.65 | 0.00 | 0.84 | 0.83 | 13.80 |
| Offshore | MK | Summer | DIC_umol_kg | 0.99 | 0.08 | 11.80 | 0.00 | 0.86 | 0.86 | 12.32 |
| Offshore | MK | Fall | DIC_umol_kg | 0.43 | 0.09 | 5.03 | 0.00 | 0.47 | 0.45 | 9.97 |
| Offshore | LK | Winter | DIC_umol_kg | 0.48 | 0.09 | 5.28 | 0.00 | 0.47 | 0.45 | 10.09 |
| Offshore | LK | Spring | DIC_umol_kg | 0.61 | 0.08 | 7.90 | 0.00 | 0.61 | 0.60 | 11.79 |
| Offshore | LK | Summer | DIC_umol_kg | 0.89 | 0.14 | 6.41 | 0.00 | 0.60 | 0.59 | 12.52 |
| Offshore | LK | Fall | DIC_umol_kg | 0.30 | 0.08 | 3.52 | 0.00 | 0.24 | 0.22 | 7.99 |
| Oceanic | UK | Winter | DIC_umol_kg | 0.74 | 0.10 | 7.49 | 0.00 | 0.90 | 0.89 | 4.81 |
| Oceanic | UK | Spring | DIC_umol_kg | 0.17 | 0.21 | 0.82 | 0.44 | 0.09 | -0.04 | 5.84 |
| Oceanic | UK | Summer | DIC_umol_kg | 0.86 | 0.31 | 2.79 | 0.05 | 0.66 | 0.58 | 11.68 |
| Oceanic | UK | Fall | DIC_umol_kg | 0.33 | 0.22 | 1.51 | 0.18 | 0.28 | 0.16 | 10.94 |
| Oceanic | MK | Winter | DIC_umol_kg | 0.14 | 0.09 | 1.61 | 0.13 | 0.15 | 0.09 | 11.35 |
| Oceanic | MK | Spring | DIC_umol_kg | -0.16 | 0.09 | -1.74 | 0.10 | 0.15 | 0.10 | 14.02 |
| Oceanic | MK | Summer | DIC_umol_kg | 0.79 | 0.11 | 7.49 | 0.00 | 0.80 | 0.79 | 6.42 |
| Oceanic | MK | Fall | DIC_umol_kg | 0.32 | 0.12 | 2.60 | 0.02 | 0.27 | 0.23 | 10.20 |
| Oceanic | LK | Winter | DIC_umol_kg | 0.45 | 0.15 | 3.02 | 0.01 | 0.35 | 0.31 | 11.24 |
| Oceanic | LK | Spring | DIC_umol_kg | 0.51 | 0.11 | 4.73 | 0.00 | 0.53 | 0.50 | 8.38 |
| Oceanic | LK | Summer | DIC_umol_kg | 0.66 | 0.13 | 4.94 | 0.00 | 0.69 | 0.66 | 7.63 |
| Oceanic | LK | Fall | DIC_umol_kg | 0.27 | 0.12 | 2.18 | 0.04 | 0.22 | 0.17 | 7.33 |
#write.csv (TA_DIC_coef_Season_noExtremes, "FLK_results/Fig_5_TA_DIC_slopes_Reefs.csv", row.names = FALSE)
TA_DIC_coef_Season_noExtremes <- FLK.data[FLK.data$Extreme=="Normal", ] %>%
group_by(Zone, Region, Sub_region, Season, Transect) %>%
do({model = lm(TA_umol_kg~DIC_umol_kg, data=.)
# create model
data.frame(tidy(model), # get coefficient info
glance(model))}) # get model infoSlopes_season<-subset(TA_DIC_coef_Season_noExtremes, term!="(Intercept)")
Slope_plot<-ggplot(data=Slopes_season, aes (x =Zone, y =estimate)) +
MyTheme+
Zone_shapes4+
geom_boxplot(aes(fill=Season), alpha=0.5,
position=position_dodge(width=0.3))+
geom_point(aes(fill = factor(Season)),
position=position_dodge(width=0.3),
shape = 21, size=0.5)+
scale_y_continuous(#limits = c(1700,2900),
#expand = c(0, 0),
#breaks = seq(1800, 2800, 100),
expression(paste("TA vs DIC slope")))+
facet_grid(Sub_region~Region)+
Season_fill+
Season_colour+
guides(fill=FALSE)+
theme(axis.text.x = element_text(angle = 90, vjust = 0.1),
legend.position = "bottom")
#Slope_plot
Figure 6: TA (µmol kg−1) vs DIC (µmol kg−1) slopes per season and reef
5 Seasonal plots
Data for endmember comparisons
# Reef
WS.reef<-subset(FLK.data, FLK.data$Zone!="Oceanic")
WS.reef$Date<-as.Date(WS.reef$Date, "%Y-%m-%d")
# Open
WS.open<-subset(FLK.data_filtered,
FLK.data_filtered$Zone=="Oceanic")
WS.open$Date<-as.Date(WS.open$Date, "%Y-%m-%d")
#summary(WS.open)# Mean ocean values by month - year
mean_Open_MY<- ddply (WS.open, .(MY, Month),
summarise,
OTem_my = mean (Temperature_C, na.rm = T),
OSal_my = mean (BestSalinity, na.rm = T),
OTA_my = mean (TA_umol_kg, na.rm = T),
OnTA_my = mean (n35TA, na.rm = T),
ODIC_my = mean (DIC_umol_kg, na.rm = T),
OnDIC_my = mean (n35DIC, na.rm = T),
OpCO2_O = mean (pCO2_uatm , na.rm = T),
OpH_O = mean (pH_calculated, na.rm = T),
OAra_O = mean (Aragonite_Sat_W , na.rm = T)
)
# Mean ocean values by month
mean_OpM <- mean_Open_MY %>%
group_by(Month) %>%
summarise(
OTem_m = mean (OTem_my, na.rm = T),
OSal_m = mean (OSal_my, na.rm = T),
OTA_m = mean (OTA_my, na.rm = T),
OnTA_m = mean (OnTA_my, na.rm = T),
OnDIC_m = mean (OnDIC_my, na.rm = T),
ODIC_m = mean (ODIC_my, na.rm = T),
OpCO2_m = mean (OpCO2_O , na.rm = T),
OpH_m = mean (OpH_O, na.rm = T),
OAra_m = mean (OAra_O, na.rm = T),
n = n()
)
#mean_OpM
# Mean ocean values by year
mean_Op<- mean_OpM %>%
summarise(
OTem = mean (OTem_m, na.rm = T),
OSal = mean (OSal_m, na.rm = T),
OTA = mean (OTA_m, na.rm = T),
OnTA = mean (OnTA_m, na.rm = T),
OnDIC = mean (OnDIC_m, na.rm = T),
ODIC = mean (ODIC_m, na.rm = T),
OpCO2 = mean (OpCO2_m, na.rm = T),
OpH = mean (OpH_m, na.rm = T),
OAra = mean (OAra_m, na.rm = T),
n = n()
)5.1 Month raw values
- Months stats
Month_Stats<- ddply (FLK.data_filtered, .(Zone, Sub_region, Season, Month2, Region),
summarise,
Tem_mean = mean (Temperature_C, na.rm = T),
Tem_sd = sd (Temperature_C, na.rm = T),
#Tem_se = std.error (Temperature_C, na.rm = T),
Sal_mean = mean (BestSalinity, na.rm = T),
Sal_sd = sd (BestSalinity, na.rm = T),
#Sal_se = std.error (BestSalinity, na.rm = T),
TA_mean = mean (TA_umol_kg, na.rm = T),
TA_sd = sd (TA_umol_kg, na.rm = T),
#TA_se = std.error (TA_umol_kg, na.rm = T),
nTA_mean = mean (n35TA, na.rm = T),
nTA_sd = sd (n35TA, na.rm = T),
DIC_mean = mean (DIC_umol_kg, na.rm = T),
DIC_sd = sd (DIC_umol_kg, na.rm = T),
#DIC_se = std.error (DIC_umol_kg, na.rm = T),
nDIC_mean = mean (n35DIC, na.rm = T),
nDIC_sd = sd (n35DIC, na.rm = T),
pCO2_mean = mean (pCO2_uatm , na.rm = T),
pCO2_sd = sd (pCO2_uatm , na.rm = T),
#pCO2_se = std.error (pCO2_uatm, na.rm = T),
pH_mean = mean (pH_calculated, na.rm = T),
pH_sd = sd (pH_calculated, na.rm = T),
#pH_se = std.error (pH_calculated, na.rm = T),
Ara_mean = mean (Aragonite_Sat_W , na.rm = T),
Ara_sd = sd (Aragonite_Sat_W , na.rm = T),
#Ara_se = std.error (Aragonite_Sat_W, na.rm = T),
n = n())
Month_Stats_2d<-Month_Stats %>%
mutate_if(is.numeric, round, digits=3)
Month_Stats_2d#write.csv(Month_Stats, "FLK_results/Table_S7_Month_Stats.csv", row.names = F)Temp
Month_Temp<- ddply (FLK.data_filtered, .(Zone, Sub_region, Season, Month2, Region), summarise,
mean = mean (Temperature_C, na.rm = T),
sd = sd (Temperature_C, na.rm = T))
Month_Temp<-Month_Temp %>%
mutate_if(is.numeric, round, digits=2)
Month_Temp$Region<-NULL
Month_Temp<-reshape(Month_Temp, idvar = c("Sub_region",
"Season", "Month2"),
timevar = "Zone", direction = "wide")Sal
Month_Sal<- ddply (FLK.data_filtered, .(Zone, Sub_region, Season, Month2, Region), summarise,
mean = mean (BestSalinity, na.rm = T),
sd = sd (BestSalinity, na.rm = T))
Month_Sal<-Month_Sal %>%
mutate_if(is.numeric, round, digits=2)
Month_Sal$Region<-NULL
Month_Sal<-reshape(Month_Sal, idvar = c("Sub_region",
"Season", "Month2"),
timevar = "Zone", direction = "wide")TA
Month_TA<- ddply (FLK.data_filtered, .(Zone, Sub_region, Season, Month2, Region), summarise,
mean = mean (TA_umol_kg, na.rm = T),
sd = sd (TA_umol_kg, na.rm = T))
Month_TA<-Month_TA %>%
mutate_if(is.numeric, round, digits=2)
Month_TA$Region<-NULL
Month_TA<-reshape(Month_TA, idvar = c("Sub_region",
"Season", "Month2"),
timevar = "Zone", direction = "wide")DIC
Month_DIC<- ddply (FLK.data_filtered, .(Zone, Sub_region, Season, Month2, Region), summarise,
mean = mean (DIC_umol_kg, na.rm = T),
sd = sd (DIC_umol_kg, na.rm = T))
Month_DIC<-Month_DIC %>%
mutate_if(is.numeric, round, digits=2)
Month_DIC$Region<-NULL
Month_DIC<-reshape(Month_DIC, idvar = c("Sub_region",
"Season", "Month2"),
timevar = "Zone", direction = "wide")pCO2
Month_pCO2<- ddply (FLK.data_filtered, .(Zone, Sub_region, Season, Month2, Region), summarise,
mean = mean (pCO2_uatm , na.rm = T),
sd = sd (pCO2_uatm , na.rm = T))
Month_pCO2<-Month_pCO2 %>%
mutate_if(is.numeric, round, digits=2)
Month_pCO2$Region<-NULL
Month_pCO2<-reshape(Month_pCO2, idvar = c("Sub_region",
"Season", "Month2"),
timevar = "Zone", direction = "wide")pH
Month_pH<- ddply (FLK.data_filtered, .(Zone, Sub_region, Season, Month2, Region), summarise,
mean = mean (pH_calculated, na.rm = T),
sd = sd (pH_calculated, na.rm = T))
Month_pH<-Month_pH %>%
mutate_if(is.numeric, round, digits=2)
Month_pH$Region<-NULL
Month_pH<-reshape(Month_pH, idvar = c("Sub_region",
"Season", "Month2"),
timevar = "Zone", direction = "wide")Arag
Month_Ara<- ddply (FLK.data_filtered, .(Zone, Sub_region, Season, Month2, Region), summarise,
mean = mean (Aragonite_Sat_W , na.rm = T),
sd = sd (Aragonite_Sat_W , na.rm = T))
Month_Ara<-Month_Ara %>%
mutate_if(is.numeric, round, digits=2)
Month_Ara$Region<-NULL
Month_Ara<-reshape(Month_Ara, idvar = c("Sub_region",
"Season", "Month2"),
timevar = "Zone", direction = "wide")all
Month_Stats3<-rbind(Month_Temp, Month_Sal, Month_TA, Month_DIC,
Month_pCO2, Month_pH, Month_Ara)
#write.csv(Month_Stats3, "FLK_results/Table_S7_Month_Stats_wide.csv", row.names = F)Seasonal range
Seasonal_range<-ddply (Month_Stats, .(Zone, Sub_region),
summarise,
Tem_meany = mean (Tem_mean, na.rm = T),
Tem_sdy = sd (Tem_mean, na.rm = T),
Tem_max = max (Tem_mean, na.rm = T),
Tem_min = min (Tem_mean, na.rm = T),
Tem_diff = (max (Tem_mean, na.rm = T) - min (Tem_mean, na.rm = T)),
Sal_meany = mean (Sal_mean, na.rm = T),
Sal_sdy = sd (Sal_mean, na.rm = T),
Sal_max = max (Sal_mean, na.rm = T),
Sal_min = min (Sal_mean, na.rm = T),
Sal_diff = (max (Sal_mean, na.rm = T) - min (Sal_mean, na.rm = T)),
TA_meany = mean (TA_mean, na.rm = T),
TA_sdy = sd (TA_mean, na.rm = T),
TA_max = max (TA_mean, na.rm = T),
TA_min = min (TA_mean, na.rm = T),
TA_diff = (max (TA_mean, na.rm = T) - min (TA_mean, na.rm = T)),
DIC_meany = mean (DIC_mean, na.rm = T),
DIC_sdy = sd (DIC_mean, na.rm = T),
DIC_max = max (DIC_mean, na.rm = T),
DIC_min = min (DIC_mean, na.rm = T),
DIC_diff = (max (DIC_mean, na.rm = T) - min (DIC_mean, na.rm = T)),
pCO2_meany = mean (pCO2_mean, na.rm = T),
pCO2_sdy = sd (pCO2_mean, na.rm = T),
pCO2_max = max (pCO2_mean, na.rm = T),
pCO2_min = min (pCO2_mean, na.rm = T),
pCO2_dif = (max (pCO2_mean, na.rm = T) - min (pCO2_mean, na.rm = T)),
pH_meany = mean (pH_mean, na.rm = T),
pH_sdy = sd (pH_mean, na.rm = T),
pH_max = max (pH_mean, na.rm = T),
pH_min = min (pH_mean, na.rm = T),
pH_diff = (max (pH_mean, na.rm = T) - min (pH_mean, na.rm = T)),
Arg_meany = mean (Ara_mean, na.rm = T),
Arg_sdy = sd (Ara_mean, na.rm = T),
Ara_max = max (Ara_mean, na.rm = T),
Ara_min = min (Ara_mean, na.rm = T),
Ara_diff = (max (Ara_mean, na.rm = T) - min (Ara_mean, na.rm = T)),
nTA_max = max (nTA_mean, na.rm = T),
nTA_min = min (nTA_mean, na.rm = T),
nTA_diff = (max (nTA_mean, na.rm = T) - min (nTA_mean, na.rm = T)),
nDIC_max = max (nDIC_mean, na.rm = T),
nDIC_min = min (nDIC_mean, na.rm = T),
nDIC_diff = (max (nDIC_mean, na.rm = T) - min (nDIC_mean, na.rm = T)),
n = n())
Seasonal_range<-Seasonal_range %>%
mutate_if(is.numeric, round, digits=2)
Seasonal_range#Seasonal_range$pH_max_diff<-Seasonal_range$pH_max-mean()
#write.csv(Seasonal_range, "FLK_results/Table_S8_Month_mean_range.csv", row.names = F)- Month Plots
Month_ploth<- ggplot(FLK.data) +
MyTheme +
facet_grid(Sub_region~Zone)+
scale_shape_manual(values=c(24, 25, 23, 22, 21))+
guides(fill=guide_legend(override.aes=list(shape=21)))+
theme(axis.title.x = element_blank() )
Year_ploth<- ggplot(Month_Stats) +
MyTheme +
theme(axis.title.x = element_blank(), axis.text.x=element_text(angle = 10, vjust = 0.5))+
Season_fill+
Season_colour+
facet_grid(Sub_region~Region)
#scale_shape_manual(values=c(24, 25, 21))+
#guides(fill=guide_legend(override.aes=list(shape=21)))+
#theme(axis.title.x = element_blank() )
Month_Season_plotTA_DIC<- ggplot(FLK.data_filtered[
FLK.data_filtered$Zone!="Oceanic",]) +
MyTheme +
facet_grid(~Zone)+
guides(fill=guide_legend(override.aes=list(shape=21)))+
scale_x_continuous(limits = c(1,12),
expand = c(0.05, 0.05),
breaks = seq(1, 12, 1),
name=expression(paste("Month of the year")))+
theme(#strip.text = element_blank(),
legend.position = "none",
axis.title.x = element_blank()
)
Month_Season_plot<- ggplot(FLK.data_filtered) +
MyTheme +
facet_grid(~Zone)+
Zone_shapes4+
#Region_fill+
#Region_colour+
guides(fill=guide_legend(override.aes=list(shape=21)))+
scale_x_continuous(limits = c(1,12),
expand = c(0.05, 0.05),
breaks = seq(1, 12, 1),
name=expression(paste("Month of the year")))+
theme(axis.title.x = element_blank())#,
# legend.position = "none")
Year_plot_2<- ggplot(Month_Stats)+
#[Month_Stats$Zone!="Oceanic",]) +
MyTheme +
facet_grid(~Zone)+
Zone_shapes4+
guides(fill=guide_legend(override.aes=list(shape=21)))+
#Season_fill+
#Season_colour+
theme(#axis.title.x = element_blank()#,
#axis.text.y=element_blank(),
legend.position = "bottom"
)
Year_plot_3<- ggplot(Month_Stats, aes(fill=Sub_region, shape=Zone))+
#[Month_Stats$Zone!="Oceanic",]) +
MyTheme +
Zone_shapes4+
facet_grid(~Region)+
guides(fill=guide_legend(override.aes=list(shape=21)))+
theme(#axis.title.x = element_blank()#,
#axis.text.y=element_blank(),
legend.position = "right",
axis.title.x = element_blank(),
axis.text.x = element_text(angle = 20, vjust = 0.5)
)Temperature
Figure Supp 2: Temperature variation by the region and shelf position by (A) month of the year and (B) season.
Temp_exp<- Month_Season_plot+
geom_abline(slope = 0, intercept = mean_Op$OTem, color="grey")+
scale_y_continuous(#limits = c(15,40),
name=(expression("Temperature"~(degree*C))),
breaks = seq(16, 40, 2),
expand = c(0.05, 0.05)) +
geom_smooth(aes (x=as.numeric(Month2),
y=Temperature_C, colour=Sub_region),
span=0.6, alpha=0.2, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=Temperature_C, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.3))+
stat_summary(aes (x=as.numeric(Month2),
y=Temperature_C, fill=Sub_region, shape=Zone),
size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.3))
#Temp_expSalinity
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 25.36 31.99 34.03 33.87 36.06 39.43
Figure Supp 3: Salinity variation by the region and shelf position by (A) month of the year and (B) season.
Sal_exp<- Month_Season_plot+
geom_abline(slope = 0, intercept = mean_Op$OSal, color="grey")+
scale_y_continuous(#limits = c(15,40),
name=(expression("Salinity")),
breaks = seq(15, 40, 1),
expand = c(0.05, 0.05)) +
geom_smooth(aes (x=as.numeric(Month2),
y=BestSalinity, colour=Sub_region),
span=0.8, alpha=0.2, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=BestSalinity, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.3))+
stat_summary(aes (x=as.numeric(Month2),
y=BestSalinity, fill=Sub_region, shape=Zone),
size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.3))
#Sal_expTA
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2214 2609 2730 2721 2851 3052
Figure Supp XX: Total Alkalinity (TA) variation by the region and shelf position by (A) month of the year and (B) season.
Figure 3A
TA_exp<- Month_Season_plot+
geom_abline(slope = 0, intercept = mean_Op$OTA, color="grey")+
scale_y_continuous(#limits = c(2100,2500),
expand = c(0.05, 0.05),
breaks = seq(2100, 2500, 100),
name=expression(paste("TA (", mu, "mol ", kg^-1, ")")))+
# geom_smooth(data=FLK.data, se=F, colour="gray70", alpha=0.5, size=0.5,
# aes(x=as.numeric(Month2), y=Temp.adj))+
geom_smooth(aes (x=as.numeric(Month2),
y=TA_umol_kg, colour=Sub_region),
span=0.8, alpha=0.2, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=TA_umol_kg, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.3))+
stat_summary(aes (x=as.numeric(Month2),
y=TA_umol_kg, fill=Sub_region, shape=Zone),
size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.3))
#
TA_Y2<- Year_plot_2+
geom_abline(slope = 0, intercept = mean_Op$OTA, color="grey")+
scale_y_continuous(#limits = c(2150,2550),
expand = c(0.05, 0.05),
breaks = seq(2200, 2500, 50),
name=expression(paste("TA (", mu, "mol ", kg^-1, ")")))+
stat_summary(aes (x=Sub_region, y=TA_mean, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2)+
stat_summary(aes (x=Sub_region, y=TA_mean, fill=Sub_region, shape=Zone),
fun.data = "mean_cl_boot", geom = "point",
size=2)
#TA_Y2
TA_Y3<- Year_plot_3+
geom_abline(slope = 0, intercept = mean_Op$OTA, color="grey")+
scale_y_continuous(limits = c(2130,2510),
expand = c(0.05, 0.05),
breaks = seq(2100, 2500, 100),
name=expression(paste("TA (", mu, "mol ", kg^-1, ")")))+
geom_point(data=Seasonal_range, aes (x=Zone, y=TA_max,
shape=Zone, colour=Sub_region),
alpha=0.8, size=1,
position = position_dodge(width = 0.6))+
geom_point(data=Seasonal_range, aes (x=Zone, y=TA_min,
shape=Zone, colour=Sub_region),
alpha=0.8, size=1,
position = position_dodge(width = 0.6))+
# geom_segment(data=Seasonal_range,
# aes( x = Zone, y = TA_min,
# xend = Zone, yend = TA_max,
# colour=Sub_region),
# #position = position_dodge(width = 0.6),
# alpha=0.3)+
stat_summary(aes (x=Zone, y=TA_mean, colour=Sub_region),
fun.data = "mean_sdl", fun.args = list(mult = 1),
geom = "errorbar", width = 0.2,
position = position_dodge(width = 0.6))+
stat_summary(aes (x=Zone, y=TA_mean, fill=Sub_region, shape=Zone),
fun = "mean",
geom = "point", size=2,
position = position_dodge(width = 0.6))
#TA_Y3
Figure 3A: Total Alkalinity (TA) variation by the region and shelf position. Left: monthly mean values, Right: Yearly mean values
DIC
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1791 2133 2238 2230 2342 2516
Figure Supp XX: Disolved Inorganic Carbon (DIC) variation by the region and shelf position by (A) month of the year and (B) season.
Figure 3B
DIC_exp<- Month_Season_plot+
geom_abline(slope = 0, intercept = mean_Op$ODIC, color="grey")+
scale_y_continuous(#limits = c(1780,2270),
expand = c(0.05, 0.05),
breaks = seq(1800, 2800, 100),
name=expression(paste("DIC (", mu, "mol ", kg^-1, ")")))+
geom_smooth(aes (x=as.numeric(Month2),
y=DIC_umol_kg, colour=Sub_region),
span=0.8, alpha=0.2, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=DIC_umol_kg, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width=0.2,
position=position_dodge(width=0.3))+
stat_summary(aes (x=as.numeric(Month2),
y=DIC_umol_kg, fill=Sub_region, shape=Zone),
size=1,
fun = "mean", geom = "point",
position=position_dodge(width=0.3))
#DIC_exp
DIC_Y2<- Year_plot_2+
geom_abline(slope = 0, intercept = mean_Op$ODIC, color="grey")+
scale_y_continuous(#limits = c(1700,2900),
expand = c(0.05, 0.05),
#breaks = seq(1800, 2800, 100),
name=expression(paste("DIC (", mu, "mol ", kg^-1, ")")))+
stat_summary(aes (x=Sub_region, y=DIC_mean, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar", width = 0.2)+
stat_summary(aes (x=Sub_region, y=DIC_mean, fill=Sub_region, shape=Zone),
fun.data = "mean_cl_boot", geom = "point",
size=2)
#DIC_Y2
DIC_Y3<- Year_plot_3+
geom_abline(slope = 0, intercept = mean_Op$ODIC, color="grey")+
scale_y_continuous(limits = c(1820,2230),
expand = c(0.00, 0.00),
breaks = seq(1800, 2800, 100),
name=expression(paste("DIC (", mu, "mol ", kg^-1, ")")))+
geom_point(data=Seasonal_range, aes (x=Zone, y=DIC_max,
shape=Zone, colour=Sub_region),
alpha=0.8, size=1,
position = position_dodge(width = 0.6))+
geom_point(data=Seasonal_range, aes (x=Zone, y=DIC_min,
shape=Zone, colour=Sub_region),
alpha=0.8, size=1,
position = position_dodge(width = 0.6))+
# geom_segment(data=Seasonal_range,
# aes( x = Zone, y = DIC_min,
# xend = Zone, yend = DIC_max,
# colour=Sub_region),
# #position = position_dodge(width = 0.6),
# alpha=0.3)+
stat_summary(aes (x=Zone, y=DIC_mean, colour=Sub_region),
fun.data = "mean_sdl", fun.args = list(mult = 1),
geom = "errorbar", width = 0.2,
position = position_dodge(width = 0.6))+
stat_summary(aes (x=Zone, y=DIC_mean, fill=Sub_region, shape=Zone),
fun = "mean",
geom = "point", size=2,
position = position_dodge(width = 0.6))
#DIC_Y3
Figure 3B: Dissolved Inorganic Carbon (DIC) by the region and shelf position. Left: monthly mean values, Right: Yearly mean values
pCO2
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 496.6 880.6 998.7 989.1 1116.0 1311.3
Figure Supp 6: pCO2 variation by the region and shelf position by (A) month of the year and (B) season.
pCO2_exp<- Month_Season_plot+
geom_abline(slope = 0, intercept = mean_Op$OpCO2, color="grey")+
scale_y_continuous(#limits = c(1,5.5),
name=expression(paste(pCO[2], " (", mu, " atm)")),
breaks = seq(200, 600, 100),
expand = c(0.05, 0.05)
) +
geom_smooth(aes (x=as.numeric(Month2),
y=pCO2_uatm, colour=Sub_region),
span=0.8, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=pCO2_uatm, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.2))+
stat_summary(aes (x=as.numeric(Month2),
y=pCO2_uatm, fill=Sub_region, shape=Zone),
size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.2))
#pCO2_exp
pCO2_Y2<- Year_plot_2+
geom_abline(slope = 0, intercept = mean_Op$OpCO2, color="grey")+
scale_y_continuous(#limits = c(1,5.5),
name=expression(paste(pCO[2],
" (", mu, " atm)")),
breaks = seq(200, 600, 50),
expand = c(0.05, 0.05)
) +
stat_summary(aes (x=Sub_region, y=pCO2_mean, fill=Sub_region, shape=Zone),
fun.data = "mean_cl_boot", geom = "point",
size=2)+
stat_summary(aes (x=Sub_region, y=pCO2_mean, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar", width = 0.2)
#pCO2_Y2
pH
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 7.953 8.235 8.322 8.315 8.408 8.552
Figure Supp 7: pH variation by the region and shelf position by (A) month of the year and (B) season.
pH_exp<- Month_Season_plot+
geom_abline(slope = 0, intercept = mean_Op$OpH, color="grey")+
scale_y_continuous(#limits = c(7.88,8.25),
name=(expression (pH)),
breaks = seq(0, 9, 0.1),
expand = c(0.02, 0)
) +
geom_smooth(aes (x=as.numeric(Month2),
y=pH_calculated, colour=Sub_region),
span=0.8, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=pH_calculated, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.2))+
stat_summary(aes (x=as.numeric(Month2),
y=pH_calculated, fill=Sub_region, shape=Zone),
size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.2))
#pH_exp
pH_Y2<- Year_plot_2+
geom_abline(slope = 0, intercept = mean_Op$OpH, color="grey")+
scale_y_continuous(#limits = c(1,5.5),
name=(expression (pH[calculated])),
breaks = seq(0, 9, 0.05),
expand = c(0.05, 0.05)
) +
stat_summary(aes (x=Sub_region, y=pH_mean, fill=Sub_region, shape=Zone),
fun.data = "mean_cl_boot", geom = "point",
size=2)+
stat_summary(aes (x=Sub_region, y=pH_mean, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar", width = 0.2)
#pH_Y2
pH_Y3<- Year_plot_3+
geom_abline(slope = 0, intercept = mean_Op$OpH, color="grey")+
scale_y_continuous(limits = c(7.87,8.22),
name=(expression (pH)),
breaks = seq(0, 9, 0.1),
expand = c(0.02, 0)
)+
geom_point(data=Seasonal_range, aes (x=Zone, y=pH_max,
shape=Zone, colour=Sub_region),
alpha=0.8, size=1,
position = position_dodge(width = 0.6))+
geom_point(data=Seasonal_range, aes (x=Zone, y=pH_min,
shape=Zone, colour=Sub_region),
alpha=0.8, size=1,
position = position_dodge(width = 0.6))+
# geom_segment(data=Seasonal_range,
# aes( x = Zone, y = pH_min,
# xend = Zone, yend = pH_max,
# colour=Sub_region),
# #position = position_dodge(width = 0.6),
# alpha=0.3)+
stat_summary(aes (x=Zone, y=pH_mean, colour=Sub_region),
fun.data = "mean_sdl", fun.args = list(mult = 1),
geom = "errorbar", width = 0.2,
position = position_dodge(width = 0.6))+
stat_summary(aes (x=Zone, y=pH_mean, fill=Sub_region, shape=Zone),
fun = "mean",
geom = "point", size=2,
position = position_dodge(width = 0.6))
#pH_Y3Figure 3C
Omega
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.715 4.277 4.758 4.719 5.235 6.029
Figure Supp 8: Omega variation by the region and shelf position by (A) month of the year and (B) season.
Figure 3D
Omega_exp<- Month_Season_plot+
geom_abline(slope = 0, intercept = mean_Op$OAra, color="grey")+
scale_y_continuous(#limits = c(1,5.5),
name=(expression("\n" ~Omega[Ar])),
breaks = seq(1, 5, 0.5),
expand = c(0.05, 0.05)
) +
geom_smooth(aes (x=as.numeric(Month2),
y=Aragonite_Sat_W, colour=Sub_region),
span=0.8, alpha=0.2, se=F
#method = lm, formula = y ~ splines::bs(x, 3),
)+
stat_summary(aes (x=as.numeric(Month2),
y=Aragonite_Sat_W, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.3))+
stat_summary(aes (x=as.numeric(Month2),
y=Aragonite_Sat_W, fill=Sub_region, shape=Zone),
size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.3))
#Omega_exp
Om_Y2<- Year_plot_2+
geom_abline(slope = 0, intercept = mean_Op$OAra, color="grey")+
scale_y_continuous(#limits = c(1,5.5),
# name=(expression (
# "Aragonite saturation state "(~Omega[Ar]))),
name=(expression (
~Omega[Ar])),
breaks = seq(1, 5, 0.5),
expand = c(0.05, 0.05)
) +
stat_summary(aes (x=Sub_region, y=Ara_mean, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar", width = 0.2)+
stat_summary(aes (x=Sub_region, y=Ara_mean, fill=Sub_region, shape=Zone),
fun.data = "mean_cl_boot", geom = "point",
size=2)
#Om_Y2
Om_Y3<- Year_plot_3+
geom_abline(slope = 0, intercept = mean_Op$OAra , color="grey")+
scale_y_continuous(#limits = c(1,5.5),
# name=(expression (
# "Aragonite saturation state "(~Omega[Ar]))),
name=(expression (
~Omega[Ar])),
breaks = seq(1, 5, 0.5),
expand = c(0.1, 0.1)
) +
geom_point(data=Seasonal_range, aes (x=Zone, y=Ara_max,
shape=Zone, colour=Sub_region),
alpha=0.8, size=1,
position = position_dodge(width = 0.6))+
geom_point(data=Seasonal_range, aes (x=Zone, y=Ara_min,
shape=Zone, colour=Sub_region),
alpha=0.8, size=1,
position = position_dodge(width = 0.6))+
# geom_segment(data=Seasonal_range,
# aes( x = Zone, y = Ara_min,
# xend = Zone, yend = Ara_max,
# colour=Sub_region),
# #position = position_dodge(width = 0.6),
# alpha=0.3)+
stat_summary(aes (x=Zone, y=Ara_mean, colour=Sub_region),
fun.data = "mean_sdl", fun.args = list(mult = 1),
geom = "errorbar", width = 0.2,
position = position_dodge(width = 0.6))+
stat_summary(aes (x=Zone, y=Ara_mean, fill=Sub_region, shape=Zone),
fun = "mean",
geom = "point", size=2,
position = position_dodge(width = 0.6))
#Om_Y3
Figure 3C: Aragonite saturation state (omega) by the region and shelf position. Left: monthly mean values, Right: Yearly mean values + SD, maximum monthly mean and minimum monthly mean.
Fig.3: Seasonal monthly plot
No summary
with summary
Fig.3: Seasonal monthly plot supplemental
Figure 3: Monthly seasonal patterns in (A) Total Alkalinity (TA), (B) Dissolved Inorganic Carbon (DIC), and (C) aragonite saturation state (Ωar) in the Florida Reef Tract. The colors represent the biogeographical regions and the vertical panels separate the locations by shelf zone. The points show the (monthly mean values ± 95 CI) and the lines the smoothed curves for the data (method = ‘loess’ and formula ‘y ~ x’, span=0.8). Gray horizontal lines in each panel demarcate the mean values of the parameters in the oceanic samples.
5.2 Season raw values
- Season stats (all data)
- do not use for further calculations since some months have more data than
- can be used to compare the aggregated meas ussing MM
Seasonal_Stats<- ddply (FLK.data_filtered, .(Zone, Sub_region, Season),
summarise,
Tem_mean = mean (Temperature_C, na.rm = T),
Tem_sd = sd (Temperature_C, na.rm = T),
Sal_mean = mean (BestSalinity, na.rm = T),
Sal_sd = sd (BestSalinity, na.rm = T),
TA_mean = mean (TA_umol_kg, na.rm = T),
TA_sd = sd (TA_umol_kg, na.rm = T),
nTA_mean = mean (n35TA, na.rm = T),
nTA_sd = sd (n35TA, na.rm = T),
nDIC_mean = mean (n35DIC, na.rm = T),
nDIC_sd = sd (n35DIC, na.rm = T),
DIC_mean = mean (DIC_umol_kg, na.rm = T),
DIC_sd = sd (DIC_umol_kg, na.rm = T),
pCO2_mean = mean (pCO2_uatm , na.rm = T),
pCO2_sd = sd (pCO2_uatm , na.rm = T),
pH_mean = mean (pH_calculated, na.rm = T),
pH_sd = sd (pH_calculated, na.rm = T),
Ara_mean = mean (Aragonite_Sat_W , na.rm = T),
Ara_sd = sd (Aragonite_Sat_W , na.rm = T),
n())
Seasonal_Stats<-Seasonal_Stats %>%
mutate_if(is.numeric, round, digits=2)
Seasonal_Stats- Season stats (monthly mean)
Seasonal_Stats2<- ddply (Month_Stats, .(Zone, Sub_region, Season),
summarise,
Tem_M = mean (Tem_mean, na.rm = T),
#Tem_sd = sd (Tem_mean, na.rm = T),
Tem_se = std.error (Tem_mean, na.rm = T),
Sal_M = mean (Sal_mean, na.rm = T),
#Sal_sd = sd (Sal_mean, na.rm = T),
Sal_se = std.error (Sal_mean, na.rm = T),
TA_M = mean (TA_mean, na.rm = T),
#TA_sd = sd (TA_mean, na.rm = T),
TA_se = std.error (TA_mean, na.rm = T),
DIC_M = mean (DIC_mean, na.rm = T),
#DIC_sd = sd (DIC_mean, na.rm = T),
DIC_se = std.error (DIC_mean, na.rm = T),
#nTA_M = mean (nTA_mean, na.rm = T),
#nTA_sd = sd (nTA_mean, na.rm = T),
#nDIC_M = mean (nDIC_mean, na.rm = T),
#nDIC_sd = sd (nDIC_mean, na.rm = T),
pCO2_M = mean (pCO2_mean , na.rm = T),
#pCO2_sd = sd (pCO2_mean , na.rm = T),
pCO2_se = std.error (pCO2_mean , na.rm = T),
pH_M = mean (pH_mean, na.rm = T),
#pH_sd = sd (pH_mean, na.rm = T),
pH_se = std.error (pH_mean, na.rm = T),
Ara_M = mean (Ara_mean , na.rm = T),
#Ara_sd = sd (Ara_mean , na.rm = T),
Ara_se = std.error (Ara_mean , na.rm = T),
n())
Seasonal_Stats2<-Seasonal_Stats2 %>%
mutate_if(is.numeric, round, digits=2)
Seasonal_Stats2# write.csv(Seasonal_Stats2, "FLK_results/Table_S_Season_A.csv", row.names = F)Note: SD is NA when calculating it based on monthly meand (n=3 per season)
- Yearly stats (monthly mean)
Yearly_Stats<- ddply (Month_Stats, .(Zone, Sub_region),
summarise,
Tem_Y = mean (Tem_mean, na.rm = T),
Tem_sd = sd (Tem_mean, na.rm = T),
Sal_Y = mean (Sal_mean, na.rm = T),
Sal_sd = sd (Sal_mean, na.rm = T),
TA_Y = mean (TA_mean, na.rm = T),
TA_sd = sd (TA_mean, na.rm = T),
nTA_Y = mean (nTA_mean, na.rm = T),
nTA_sd = sd (nTA_mean, na.rm = T),
nDIC_Y = mean (nDIC_mean, na.rm = T),
nDIC_sd = sd (nDIC_mean, na.rm = T),
DIC_Y = mean (DIC_mean, na.rm = T),
DIC_sd = sd (DIC_mean, na.rm = T),
pCO2_Y = mean (pCO2_mean , na.rm = T),
pCO2_sd = sd (pCO2_mean , na.rm = T),
pH_Y = mean (pH_mean, na.rm = T),
pH_sd = sd (pH_mean, na.rm = T),
Ara_Y = mean (Ara_mean , na.rm = T),
Ara_sd = sd (Ara_mean , na.rm = T),
n())
Yearly_Stats<-Yearly_Stats %>%
mutate_if(is.numeric, round, digits=2)
Yearly_Stats# write.csv(Yearly_Stats, "FLK_results/Table_1_Year_A.csv", row.names = F)Note: SD is NA when calculating it based on monthly meand (n=3 per season)
5.3 Delta months values
# 1. Get the oceanic mean values per month-year
WS.reef<-join(WS.reef, mean_Open_MY,
type = "left", by=c("MY", "Month"))
# 2. and subtract them from the reef data
# Delta TA
WS.reef$dTA_MY<-WS.reef$TA_umol_kg-WS.reef$OTA_my
# Delta DIC
WS.reef$dDIC_MY<-WS.reef$DIC_umol_kg-WS.reef$ODIC_my
# Delta nTA
WS.reef$dnTA_MY<-WS.reef$n35TA-WS.reef$OnTA_my
# Delta nDIC
WS.reef$dnDIC_MY<-WS.reef$n35DIC-WS.reef$OnDIC_my
# Delta pCO2
WS.reef$dpCO2_MY<-WS.reef$pCO2_uatm-WS.reef$OpCO2_O
# Delta pH
WS.reef$dpH_MY<-WS.reef$pH_calculated-WS.reef$OpH_O
# Delta Omega
WS.reef$dOme_MY<-WS.reef$Aragonite_Sat_W-WS.reef$OAra_O
# head(WS.reef)
#summary(WS.reef$Season, digits=2)- Delta month stats
dMonth_Stats<- ddply (WS.reef, .(Zone, Sub_region, Month2, Season, Region),
summarise,
dTA_mean = mean (dTA_MY, na.rm = T),
dTA_sd = sd (dTA_MY, na.rm = T),
#dTA_se = std.error (dTA_MY, na.rm = T),
dnTA_mean = mean (dnTA_MY, na.rm = T),
dnTA_sd = sd (dnTA_MY, na.rm = T),
dDIC_mean = mean (dDIC_MY, na.rm = T),
dDIC_sd = mean (dDIC_MY, na.rm = T),
#dDIC_se = std.error (dDIC_MY, na.rm = T),
dnDIC_mean = mean (dnDIC_MY, na.rm = T),
dnDIC_sd = sd (dnDIC_MY, na.rm = T),
dpCO2_mean = mean (dpCO2_MY , na.rm = T),
dpCO2_sd = sd (dpCO2_MY , na.rm = T),
#dpCO2_se = std.error (dpCO2_MY, na.rm = T),
dpH_mean = mean (dpH_MY, na.rm = T),
dpH_sd = sd (dpH_MY, na.rm = T),
#dPH_se = std.error (dpH_MY, na.rm = T),
dAra_mean = mean (dOme_MY , na.rm = T),
dAra_sd = sd (dOme_MY , na.rm = T)#,
#dAra_se = std.error (dOme_MY, na.rm = T),
)
dMonth_Stats<-dMonth_Stats %>%
mutate_if(is.numeric, round, digits=3)
dMonth_Stats#write.csv(dMonth_Stats, "FLK_results/Table_S9_Month_StatsDeltas.csv", row.names = F)
Spring.data<-WS.reef[WS.reef$Season=="Spring", ]
Summer.data<-WS.reef[WS.reef$Season=="Summer", ]
Fall.data<-WS.reef[WS.reef$Season=="Fall", ]
Winter.data<-WS.reef[WS.reef$Season=="Winter", ]Seasonal range
Seasonal_range_d<-ddply (dMonth_Stats, .(Zone, Sub_region),
summarise,
dTA_max = max (dTA_mean, na.rm = T),
dTA_min = min (dTA_mean, na.rm = T),
dTA_diff = (max (dTA_mean, na.rm = T) - min (dTA_mean, na.rm = T)),
dDIC_max = max (dDIC_mean, na.rm = T),
dDIC_min = min (dDIC_mean, na.rm = T),
dDIC_diff = (max (dDIC_mean, na.rm = T) - min (dDIC_mean, na.rm = T)),
dpCO2_max = max (dpCO2_mean, na.rm = T),
dpCO2_min = min (dpCO2_mean, na.rm = T),
dpCO2_dif = (max (dpCO2_mean, na.rm = T) - min (dpCO2_mean, na.rm = T)),
dpH_max = max (dpH_mean, na.rm = T),
dpH_min = min (dpH_mean, na.rm = T),
dpH_diff = (max (dpH_mean, na.rm = T) - min (dpH_mean, na.rm = T)),
dAra_max = max (dAra_mean, na.rm = T),
dAra_min = min (dAra_mean, na.rm = T),
dAra_diff = (max (dAra_mean, na.rm = T) - min (dAra_mean, na.rm = T)),
n = n())
Seasonal_range_d<-Seasonal_range_d %>%
mutate_if(is.numeric, round, digits=3)
Seasonal_range_d#write.csv(Seasonal_range_d, "FLK_results/Table_S8_Month_mean_range_d.csv", row.names = F)- Delta month plots
Delta_plot<- ggplot(WS.reef) +
MyTheme +
facet_grid(Sub_region~Zone)+
scale_shape_manual(values=c(24, 25, 23, 22, 21))+
guides(fill=guide_legend(override.aes=list(shape=21)))+
geom_hline(yintercept = 0, color="gray")+
theme(axis.title.x = element_blank())
DeltaYear_plot<- ggplot(dMonth_Stats) +
MyTheme +
facet_grid(Sub_region~Region)+
Season_fill+
Season_colour+
geom_hline(yintercept = 0, color="gray")+
theme(axis.title.x = element_blank(),
axis.text.x = element_text(angle = 10,
vjust = 0.5))
Delta_plot_month<- ggplot(WS.reef) +
MyTheme +
geom_abline(slope = 0, intercept = 0, color="grey")+
facet_grid(~Zone)+
guides(fill=guide_legend(override.aes=list(shape=21)))+
scale_x_continuous(limits = c(1,12),
expand = c(0.05, 0.05),
breaks = seq(1, 12, 1),
name=expression(paste("Month of the year")))+
theme(axis.title.x = element_blank(),
legend.position = "bottom")
DeltaYear_plot2<- ggplot(dMonth_Stats) +
MyTheme +
#facet_grid(Zone~., scales = "free_y")+
facet_grid(~Zone, scales="free" )+
geom_hline(yintercept = 0, color="gray")+
theme(axis.title.x = element_blank(),
legend.position = "bottom",
axis.text.x = element_text(angle = 0))
DeltaYear_plot3<- ggplot(dMonth_Stats) +
MyTheme +
Zone_shapes3+
facet_grid(~Region)+
geom_hline(yintercept = 0, color="gray")+
theme(axis.title.x = element_blank(),
legend.position = "bottom",
axis.text.x = element_text(angle = 0))
Frequency_plot<- ggplot(data = WS.reef) +
geom_vline(aes(xintercept = 0), linetype = "dashed")+
facet_grid(Zone~Sub_region, scales = "free_y")+ MyThemedTA
Figure Supp 4: Total Alkalinity (TA) variation by the region and shelf position by (A) month of the year and (B) season.
Figure 4A
DTA_exp<- Delta_plot_month+
scale_y_continuous(#limits = c(-350,340),
expand = c(0.05, 0.05),
breaks = seq(-800, 800, 100),
name=expression(paste(Delta,
"TA (", mu, "mol ", ~kg^-1, ")")))+
geom_smooth(aes (x=as.numeric(Month2),
y=dTA_MY, colour=Sub_region),
span=0.8, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=dTA_MY, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.2))+
stat_summary(aes (x=as.numeric(Month2),
y=dTA_MY, fill=Sub_region),
shape=21, size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.2))
#DTA_exp
TA_delta_Y2<- DeltaYear_plot2+
scale_y_continuous(#limits = c(-350,340),
expand = c(0.03, 0.03),
#breaks = seq(-800, 800, 50),
name=expression(paste(Delta, "TA (", mu, "mol ", ~kg^-1, ")")))+
stat_summary(aes (x=Season, y=dTA_mean, fill=Sub_region),
fun.data = "mean_cl_boot", geom = "bar",
position=position_dodge(width=0.8))+
stat_summary(aes (x=Season, y=dTA_mean, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.8))
#TA_delta_Y2
dTA_Y3<- DeltaYear_plot3+
scale_y_continuous(#limits = c(-210,70),
expand = c(0.03, 0.03),
#breaks = seq(0, 2500, 50),
name=expression(paste("")))+
stat_summary(aes (x=Zone, y=dTA_mean,
fill=Sub_region, shape=Zone),
fun.data = "mean_cl_boot", geom = "point",
size=1.5,
position=position_dodge(width=0.5))+
stat_summary(aes (x=Zone, y=dTA_mean, colour=Sub_region),
fun.data = "mean_cl_boot",
geom = "errorbar", width = 0.2,
position=position_dodge(width=0.5))+
facet_grid(~Region)
#dTA_Y3
Figure 4A: Seasonal delta Total Alkalinity (dTA) variation by region and shelf position. Mean values and 95 CI
dDIC
Figure Supp 5: Total Alkalinity (TA) variation by the region and shelf position by (A) month of the year and (B) season.
Figure 4B
dDIC_exp<- Delta_plot_month+
scale_y_continuous(#limits = c(1700,2900),
expand = c(0.05, 0.05),
breaks = seq(-800, 800, 100),
name=expression(paste(Delta, "DIC (", mu, "mol ", ~kg^-1, ")")))+
geom_smooth(aes (x=as.numeric(Month2),
y=dDIC_MY, colour=Sub_region),
span=0.8, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=dDIC_MY, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.2))+
stat_summary(aes (x=as.numeric(Month2),
y=dDIC_MY, fill=Sub_region),
shape=21, size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.2))
#dDIC_exp
DIC_delta_Y2<- DeltaYear_plot2+
scale_y_continuous(#limits = c(-350,340),
expand = c(0.05, 0.05),
#breaks = seq(-800, 800, 50),
name=expression(paste(Delta, "DIC (", mu, "mol ", ~kg^-1, ")")))+
stat_summary(aes (x=Season, y=dDIC_mean, fill=Sub_region),
fun.data = "mean_cl_boot", geom = "bar",
position=position_dodge(width=0.8))+
stat_summary(aes (x=Season, y=dDIC_mean, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.8))
#DIC_delta_Y2
dDIC_Y3<- DeltaYear_plot3+
scale_y_continuous(#limits = c(-185,130),
expand = c(0.01, 0.01),
#breaks = seq(0, 2500, 50),
name=expression(paste("")))+
stat_summary(aes (x=Zone, y=dDIC_mean,
fill=Sub_region, shape = Zone),
fun.data = "mean_cl_boot", geom = "point",
size=1.5,
position=position_dodge(width=0.5))+
stat_summary(aes (x=Zone, y=dDIC_mean, colour=Sub_region),
fun.data = "mean_cl_boot",
geom = "errorbar", width = 0.2,
position=position_dodge(width=0.5))
#dDIC_Y3
Figure 4B: Seasonal delta Dissolved Inorganic Carbon (dDIC) variation by region and shelf position.
dDIC density
dDIC_freq_All<- Frequency_plot+
geom_density(aes (dDIC_MY), fill = "lightgray", alpha=0.5) +
scale_x_continuous(breaks = seq(-300, 300, 100),
expression(paste(Delta, "DIC (", mu, "mol ", kg^-1, ")")))
#dDIC_freq_All
dDIC_freq_pre<- Frequency_plot+
geom_density(aes (dDIC_MY, fill = Precipitation),
alpha = 0.4) +
scale_x_continuous(breaks = seq(-300, 300, 100),
expression(paste(Delta, "DIC (", mu, "mol ", kg^-1, ")")))
#dDIC_freq_pre
# dDIC_freq_sea<- Frequency_plot+
# geom_density(aes (dDIC_MY, fill = Season), alpha = 0.4) +
# scale_x_continuous(breaks = seq(-300, 300, 100),
# expression(paste(Delta, "DIC (", mu, "mol ", kg^-1, ")")))
# dDIC_freq_seadpCO2
pdCO2_exp<- Delta_plot_month+
scale_y_continuous(#limits = c(1700,2900),
expand = c(0.05, 0.05),
#breaks = seq(1800, 2800, 100),
name=expression(paste(Delta ~ pCO[2] , " (", mu, "atm)")))+
geom_smooth(aes (x=as.numeric(Month2),
y=dpCO2_MY, colour=Sub_region),
span=0.8, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=dpCO2_MY, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.2))+
stat_summary(aes (x=as.numeric(Month2),
y=dpCO2_MY, fill=Sub_region),
shape=21, size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.2))
#pdCO2_exp
pCO2_delta_Y2<- DeltaYear_plot2+
scale_y_continuous(#limits = c(-350,340),
expand = c(0.1, 0.1),
#breaks = seq(-800, 800, 50),
name=expression(paste(Delta ~ pCO[2] , " (", mu, "atm)")))+
stat_summary(aes (x=Season, y=dpCO2_mean, fill=Sub_region),
fun.data = "mean_cl_boot", geom = "bar",
position=position_dodge(width=0.8))+
stat_summary(aes (x=Season, y=dpCO2_mean, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.8))
#pCO2_delta_Y2
dPCO2_Y3<- DeltaYear_plot2+
scale_y_continuous(#limits = c(2150,2550),
expand = c(0.01, 0.01),
#breaks = seq(0, 2500, 50),
name=expression(paste("")))+
stat_summary(aes (x=Sub_region, y=dpCO2_mean, fill=Sub_region),
fun.data = "mean_cl_boot", geom = "point",
size=1.5, shape=21)+
stat_summary(aes (x=Sub_region, y=dpCO2_mean, colour=Sub_region),
fun.data = "mean_cl_boot",
geom = "errorbar", width = 0.2)
#dPCO2_Y3
dpH
Figure 4C
dOmega
Figure 4D
DOmega_exp<- Delta_plot_month+
scale_y_continuous(#limits = c(1,5.5),
name=(expression (paste(Delta ,
~Omega[Ar]))),
breaks = seq(-3, 3, 0.5),
expand = c(0.05, 0.05)
) +
geom_smooth(aes (x=as.numeric(Month2),
y=dOme_MY, colour=Sub_region),
span=0.8, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=dOme_MY, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.2))+
stat_summary(aes (x=as.numeric(Month2),
y=dOme_MY, fill=Sub_region),
shape=21, size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.2))+
facet_grid(Zone~.)
#DOmega_exp
om_delta_Y2<- DeltaYear_plot2+
scale_y_continuous(#limits = c(-350,340),
expand = c(0.05, 0.05),
#breaks = seq(-800, 800, 50),
name=(expression (paste(Delta ,
~Omega[Ar]))))+
stat_summary(aes (x=Season, y=dAra_mean, fill=Sub_region),
fun.data = "mean_cl_boot", geom = "bar",
position=position_dodge(width=0.8))+
stat_summary(aes (x=Season, y=dAra_mean, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.8))+
theme(panel.spacing = unit(0.8, "lines"))
#om_delta_Y2
dOm_Y3<- DeltaYear_plot3+
scale_y_continuous(#limits = c(-0.9, 1),
expand = c(0.02, 0.02),
breaks = seq(-5, 5, 0.5),
name=(expression (paste(Delta ,
~Omega[Ar]))))+
stat_summary(aes (x=Zone, y=dAra_mean,
fill=Sub_region, shape=Zone),
fun.data = "mean_cl_boot", geom = "point",
size=1.5, position=position_dodge(width=0.5))+
stat_summary(aes (x=Zone, y=dAra_mean, colour=Sub_region),
fun.data = "mean_cl_boot",
geom = "errorbar", width = 0.2,
position=position_dodge(width=0.5))
#dOm_Y3
- Figure 4: Seasonal Δ and yearly sums
Seasonality_deltas<-ggarrange(
TA_delta_Y2 +theme(legend.position = "none",
axis.text.x=element_blank()),
DIC_delta_Y2+theme(legend.position = "none",
strip.text.x = element_blank(),
axis.text.x=element_blank()),
pH_delta_Y2+theme(legend.position = "none",
strip.text.x = element_blank(),
axis.text.x=element_blank()),
om_delta_Y2+ theme(panel.spacing = unit(0.8, "lines"),
strip.text.x = element_blank()),
#TA_DIC_sum,
nrow = 4,
#labels = c("A", "B", " C"),
heights =c(1, 0.9, 0.9, 1.2))
#widths = c(1, 1, 0.9))
Seasonality_deltas
Seasonality_deltas3<-ggarrange(
dTA_my3,
dDIC_my3,
dpH_my3,
dOm_my3,
nrow = 4,
#labels = c("A", "B", " C"),
heights =c(1, 0.9, 0.9, 1))
Seasonality_deltas3
#ggsave(file="FLK_results/Figures/Fig_4/Fig_4_delta_season.svg", plot=Seasonality_deltas, dpi = 300, width=6, height=6)
#ggsave(file="FLK_results/Figures/Fig_4/Fig_4_delta_ICRS.svg", plot=Seasonality_deltas, dpi = 300, width=8, height=6)
#ggsave(file="FLK_results/Figures/Fig_4/Fig_4_delta_Summary.svg", plot=Seasonality_deltas3, dpi = 300, width=8, height=8)Figure 4: Seasonal inshore-ocean gradients in the Florida Keys. (A) Seasonal ΔTA. (B) Seasonal ΔDIC. (C) Seasonal ΩAr. The bars represent the mean Δ values per season, region, and shelf position (± 95 CI).
5.4 Delta season values
- Delta season stats (all data)
dSeason_Stats<- ddply (WS.reef, .(Zone, Sub_region, Season),
summarise,
dTA_mean = mean (dTA_MY, na.rm = T),
dTA_sd = sd (dTA_MY, na.rm = T),
dnTA_mean = mean (dnTA_MY, na.rm = T),
dnTA_sd = sd (dnTA_MY, na.rm = T),
dDIC_mean = mean (dDIC_MY, na.rm = T),
dDIC_sd = mean (dDIC_MY, na.rm = T),
#dnDIC_mean = mean (dnDIC_MY, na.rm = T),
#dnDIC_sd = sd (dnDIC_MY, na.rm = T),
#dpCO2_mean = mean (dpCO2_MY , na.rm = T),
#dpCO2_sd = sd (dpCO2_MY , na.rm = T),
dpH_mean = mean (dpH_MY, na.rm = T),
dpH_sd = mean (dpH_MY, na.rm = T),
dAra_mean = mean (dOme_MY , na.rm = T),
dAra_sd = mean (dOme_MY , na.rm = T),
n())
dSeason_Stats<-dSeason_Stats %>%
mutate_if(is.numeric, round, digits=3)
dSeason_Stats- Delta season stats (monthly mean)
dSeasonal_Stats2<- ddply (dMonth_Stats,
.(Zone, Season, Sub_region),
summarise,
dTA_M = mean (dTA_mean, na.rm = T),
dTA_sd = sd (dTA_mean, na.rm = T),
dTA_se = std.error (dTA_mean, na.rm = T),
dDIC_M = mean (dDIC_mean, na.rm = T),
dDIC_sd = sd (dDIC_mean, na.rm = T),
dDIC_se = std.error (dDIC_mean, na.rm = T),
#dnTA_M = mean (dnTA_mean, na.rm = T),
#dnTA_sd = sd (dnTA_mean, na.rm = T),
#dnDIC_M = mean (dnDIC_mean, na.rm = T),
#dnDIC_sd = sd (dnDIC_mean, na.rm = T),
dpCO2_M = mean (dpCO2_mean , na.rm = T),
dpCO2_sd = sd (dpCO2_mean , na.rm = T),
dpCO2_se = std.error (dpCO2_mean , na.rm = T),
dpH_M = mean (dpH_mean, na.rm = T),
dpH_sd = sd (dpH_mean, na.rm = T),
dpH_se = std.error (dpH_mean, na.rm = T),
dAra_M = mean (dAra_mean , na.rm = T),
dAra_sd = sd (dAra_mean , na.rm = T),
dAra_se = std.error (dAra_mean , na.rm = T),
n())
dSeasonal_Stats2<-dSeasonal_Stats2 %>%
mutate_if(is.numeric, round, digits=3)
dSeasonal_Stats2#write.csv(dSeasonal_Stats2, "FLK_results/Table_2_SeasonDelta_B.csv", row.names = F)Note: SD is NA when calculating it based on monthly meand (n=3 per season)
- Delta yearly stats (monthly mean)
dYearly_Stats<- ddply (dMonth_Stats, .(Zone, Sub_region),
summarise,
dTA_M = mean (dTA_mean, na.rm = T),
dTA_sd = sd (dTA_mean, na.rm = T),
#dTA_se = std.error (dTA_mean, na.rm = T),
dDIC_M = mean (dDIC_mean, na.rm = T),
dDIC_sd = sd (dDIC_mean, na.rm = T),
#dDIC_se = std.error (dDIC_mean, na.rm = T),
dnTA_M = mean (dnTA_mean, na.rm = T),
dnTA_sd = sd (dnTA_mean, na.rm = T),
dnDIC_M = mean (dnDIC_mean, na.rm = T),
dnDIC_sd = sd (dnDIC_mean, na.rm = T),
dpCO2_M = mean (dpCO2_mean , na.rm = T),
dpCO2_sd = sd (dpCO2_mean , na.rm = T),
#dpCO2_se = std.error (dpCO2_mean , na.rm = T),
dpH_M = mean (dpH_mean, na.rm = T),
dpH_sd = sd (dpH_mean, na.rm = T),
#dpH_se = std.error (dpH_mean, na.rm = T),
dAra_M = mean (dAra_mean , na.rm = T),
dAra_sd = sd (dAra_mean , na.rm = T),
#dAra_se = std.error (dAra_mean , na.rm = T),
n())
dYearly_Stats<-dYearly_Stats %>%
mutate_if(is.numeric, round, digits=3)
dYearly_Stats#write.csv(dYearly_Stats, "FLK_results/Table_1b_delta_year.csv", row.names = F)- Yearly summ (monthly mean)
dYear_Add<- ddply (dMonth_Stats, .(Zone, Sub_region),
summarise,
su_dTA_Y = sum (dTA_mean, na.rm = T),
#m_TA_Y = mean (dTA_mean, na.rm = T),
#sd_TA_Y = sd (dTA_mean, na.rm = T),
su_dDIC_Y = sum (dDIC_mean, na.rm = T),
#m_dDIC_Y = mean (dDIC_mean, na.rm = T),
#sd_dDIC_Y = sd (dDIC_mean, na.rm = T),
su_dpCO2_Y = sum (dpCO2_mean, na.rm = T),
#m_pCO2_Y = mean (dpCO2_mean, na.rm = T),
#sd_pCO2_Y = sd (dpCO2_mean, na.rm = T),
su_dpH_Y = sum (dpH_mean, na.rm = T),
#m_pH_Y = mean (dpH_mean, na.rm = T),
#sd_pH_Y = sd (dpH_mean, na.rm = T),
su_dAra_Y = sum (dAra_mean, na.rm = T),
#m_Ara_Y = mean (dAra_mean, na.rm = T),
#sd_Ara_Y = sd (dAra_mean, na.rm = T),
n())
dYear_Add<-dYear_Add %>%
mutate_if(is.numeric, round, digits=2)
dYear_Add#write.csv(dYear_Add, "FLK_results/Table_1_year_byMM.csv", row.names = F)- Yearly summ (indiviual years)
dYear_Add2<- ddply (WS.reef, .(Zone, Sub_region, MY, Region, Year),
summarise,
dTA_mean = mean (dTA_MY, na.rm = T),
dTA_sd = sd (dTA_MY, na.rm = T),
#dTA_se = std.error (dTA_MY, na.rm = T),
dnTA_mean = mean (dnTA_MY, na.rm = T),
dnTA_sd = sd (dnTA_MY, na.rm = T),
dDIC_mean = mean (dDIC_MY, na.rm = T),
dDIC_sd = mean (dDIC_MY, na.rm = T),
#dDIC_se = std.error (dDIC_MY, na.rm = T),
dnDIC_mean = mean (dnDIC_MY, na.rm = T),
dnDIC_sd = sd (dnDIC_MY, na.rm = T),
dpCO2_mean = mean (dpCO2_MY , na.rm = T),
dpCO2_sd = sd (dpCO2_MY , na.rm = T),
#dpCO2_se = std.error (dpCO2_MY, na.rm = T),
dpH_mean = mean (dpH_MY, na.rm = T),
dpH_sd = mean (dpH_MY, na.rm = T),
#dPH_se = std.error (dpH_MY, na.rm = T),
dAra_mean = mean (dOme_MY , na.rm = T),
dAra_sd = mean (dOme_MY , na.rm = T),
#dAra_se = std.error (dOme_MY, na.rm = T),
n())
dYear_Add2<-dYear_Add2 %>%
mutate_if(is.numeric, round, digits=3)
dYear_Add2dYear_Add2<-dYear_Add2[dYear_Add2$Year>2014, ]
dYear_Add2<-dYear_Add2[dYear_Add2$Year<2022, ]
dYear_Add3<- ddply (dYear_Add2, .(Zone, Sub_region, Year),
summarise,
su_dTA_Y = sum (dTA_mean, na.rm = T),
#m_TA_Y = mean (dTA_mean, na.rm = T),
#sd_TA_Y = sd (dTA_mean, na.rm = T),
su_dDIC_Y = sum (dDIC_mean, na.rm = T),
#m_dDIC_Y = mean (dDIC_mean, na.rm = T),
#sd_dDIC_Y = sd (dDIC_mean, na.rm = T),
su_dpCO2_Y = sum (dpCO2_mean, na.rm = T),
#m_pCO2_Y = mean (dpCO2_mean, na.rm = T),
#sd_pCO2_Y = sd (dpCO2_mean, na.rm = T),
su_dpH_Y = sum (dpH_mean, na.rm = T),
#m_pH_Y = mean (dpH_mean, na.rm = T),
#sd_pH_Y = sd (dpH_mean, na.rm = T),
su_dAra_Y = sum (dAra_mean, na.rm = T),
#m_Ara_Y = mean (dAra_mean, na.rm = T),
#sd_Ara_Y = sd (dAra_mean, na.rm = T),
n())
dYear_Add3<-dYear_Add3 %>%
mutate_if(is.numeric, round, digits=2)
dYear_Add35.5 Seasonal models
season.data<-WS.reef[WS.reef$Extreme=="Normal", ]
season.data<-droplevels(season.data)
season.data$Zone<-factor(season.data$Zone, levels = c("Offshore", "Mid channel", "Inshore"))
#summary(season.data)
inshore.season.data<-season.data[season.data$Zone=="Inshore", ]
midchanel.season.data<-season.data[season.data$Zone=="Mid channel", ]
ofshore.season.data<-season.data[season.data$Zone=="Offshore", ]LMER - TA month
hist(FLK.data_filtered$TA_umol_kg)
shapiro.test(FLK.data_filtered$TA_umol_kg) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: FLK.data_filtered$TA_umol_kg
## W = 0.8038, p-value < 2.2e-16#
# dTA_season<-lmer(dTA_MY~ Zone + Sub_region + Season +
# Zone:Sub_region + Zone:Season + Sub_region:Season+
# (1|SiteID), data=season.data)
TA_month_0<-lmer(TA_umol_kg~ Zone * Sub_region * Month +
(1|SiteID), data=FLK.data_filtered)
#anova(TA_month_0)
TA_month<-lmer(TA_umol_kg~ Zone + Sub_region + Month +
Zone:Sub_region + Zone:Month + Sub_region:Month +
(1|SiteID), data=season.data)
#summary(dTA_season)
av.TA_month<-(anova(TA_month))
av.TA_month ranova(TA_month) #step(TA_month)
plot(TA_month)
par(mfrow=c(1,2))
qqnorm(resid(TA_month))
acf(resid(TA_month), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#summ(TA_month, scale = TRUE)
#plot_summs(TA_month)
TA_mo.emmc<-emmeans(TA_month, ~Zone*Month, by="Sub_region")
TA_mon.emmc<-multcomp::cld(TA_mo.emmc)
#dTA_ses.emmc<-as.data.frame(dTA_ses.emmc[complete.cases(dTA_ses.emmc),])
#TA_mon.emmc<-TA_mon.emmc[order(TA_mon.emmc$Zone, TA_mon.emmc$Month), ]
TA_mon.emmcLMER - TA season
hist(season.data$TA_umol_kg)
shapiro.test(season.data$TA_umol_kg) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: season.data$TA_umol_kg
## W = 0.83476, p-value < 2.2e-16#
# dTA_season<-lmer(dTA_MY~ Zone + Sub_region + Season +
# Zone:Sub_region + Zone:Season + Sub_region:Season+
# (1|SiteID), data=season.data)
TA_season_0<-lmer(TA_umol_kg~ Zone * Sub_region * Season +
(1|SiteID), data=season.data)
anova(TA_season_0) TA_season<-lmer(TA_umol_kg~ Zone + Sub_region + Season +
Zone:Sub_region +
Zone:Season +
Sub_region:Season +
(1|SiteID), data=season.data)
anova(TA_season_0, TA_season) #summary(av.TA_season)
av.TA_season<-(anova(TA_season))
av.TA_season ranova(TA_season) #step(TA_season)
plot(TA_season)
par(mfrow=c(1,2))
qqnorm(resid(TA_season))
acf(resid(TA_season), main="acf(resid(TA Season))")
par(mfrow=c(1,1))
#summ(av.TA_season, scale = TRUE)
#plot_summs(TA_season)
TA_se.emmc<-emmeans(TA_season, ~Zone*Season, by="Sub_region")
TA_seas.emmc<-multcomp::cld(TA_se.emmc)
TA_seas.emmc TA_se.emmc2<-emmeans(TA_season, ~Sub_region*Season, by="Zone")
TA_seas.emmc2<-multcomp::cld(TA_se.emmc2)
TA_seas.emmc2LMER - all dTA - MODEL 13
hist(season.data$dTA_MY)
shapiro.test(season.data$dTA_MY) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: season.data$dTA_MY
## W = 0.81662, p-value < 2.2e-16#
# dTA_season<-lmer(dTA_MY~ Zone + Sub_region + Season +
# Zone:Sub_region + Zone:Season + Sub_region:Season+
# (1|SiteID), data=season.data)
dTA_season<-lmer(dTA_MY~ Zone * Sub_region * Season +
(1|SiteID), data=season.data)
#summary(dTA_season)
av.dTA_season<-(anova(dTA_season))
av.dTA_season ranova(dTA_season) #step(dTA_season)
plot(dTA_season)
par(mfrow=c(1,2))
qqnorm(resid(dTA_season))
acf(resid(dTA_season), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#summ(dTA_season, scale = TRUE)
#plot_summs(dTA_season) dTA_ses.emmc<-emmeans(dTA_season, ~Season*Sub_region, by="Zone")
dTA_ses.emmc<-multcomp::cld(dTA_ses.emmc)
#dTA_ses.emmc<-as.data.frame(dTA_ses.emmc[complete.cases(dTA_ses.emmc),])
dTA_ses.emmc<-dTA_ses.emmc[order(dTA_ses.emmc$Zone,
dTA_ses.emmc$Season,
dTA_ses.emmc$Sub_region), ]
dTA_ses.emmc dTA_ses.emmc[,-c(3)] %>% kable("html", digits=2, row.names = F,
caption = "Seasonal dTA") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Offshore", 1, 16) %>% # groups rows with zone label
pack_rows(., "Mid-Channel", 17, 32) %>%
pack_rows(., "Inshore", 33, 48)| Season | Sub_region | emmean | SE | df | lower.CL | upper.CL | .group |
|---|---|---|---|---|---|---|---|
| Offshore | |||||||
| Winter | BB | 1.15 | 12.34 | 202.53 | -23.19 | 25.49 | 1 |
| Winter | UK | 4.02 | 10.57 | 233.81 | -16.81 | 24.86 | 1 |
| Winter | MK | 4.70 | 10.17 | 209.03 | -15.34 | 24.75 | 1 |
| Winter | LK | 2.05 | 8.73 | 200.55 | -15.18 | 19.27 | 1 |
| Spring | BB | -3.59 | 11.75 | 166.82 | -26.80 | 19.61 | 1 |
| Spring | UK | -1.76 | 9.99 | 195.01 | -21.47 | 17.95 | 1 |
| Spring | MK | -7.08 | 9.23 | 148.06 | -25.32 | 11.17 | 1 |
| Spring | LK | -11.23 | 7.94 | 143.27 | -26.93 | 4.47 | 1 |
| Summer | BB | -5.51 | 12.69 | 223.40 | -30.51 | 19.49 | 1 |
| Summer | UK | -4.76 | 10.79 | 252.32 | -26.00 | 16.49 | 1 |
| Summer | MK | -24.72 | 10.36 | 223.40 | -45.13 | -4.30 | 1 |
| Summer | LK | -10.43 | 8.97 | 223.40 | -28.11 | 7.24 | 1 |
| Fall | BB | -0.84 | 11.48 | 155.23 | -23.51 | 21.84 | 1 |
| Fall | UK | -0.58 | 9.99 | 196.41 | -20.28 | 19.13 | 1 |
| Fall | MK | -1.32 | 9.23 | 148.06 | -19.56 | 16.92 | 1 |
| Fall | LK | -3.36 | 8.03 | 149.52 | -19.22 | 12.50 | 1 |
| Mid-Channel | |||||||
| Winter | BB | 8.13 | 12.69 | 223.40 | -16.87 | 33.13 | 34 |
| Winter | UK | 10.78 | 13.07 | 246.09 | -14.96 | 36.52 | 34 |
| Winter | MK | 25.33 | 10.17 | 209.04 | 5.28 | 45.38 | 4 |
| Winter | LK | 13.63 | 9.37 | 155.69 | -4.88 | 32.13 | 4 |
| Spring | BB | -8.24 | 12.70 | 220.76 | -33.26 | 16.78 | 234 |
| Spring | UK | -2.67 | 12.34 | 202.49 | -27.01 | 21.66 | 234 |
| Spring | MK | -36.96 | 9.10 | 140.60 | -54.95 | -18.96 | 123 |
| Spring | LK | -48.59 | 8.86 | 127.36 | -66.13 | -31.05 | 12 |
| Summer | BB | -13.78 | 12.69 | 223.40 | -38.78 | 11.22 | 234 |
| Summer | UK | -5.59 | 13.50 | 270.08 | -32.16 | 20.99 | 234 |
| Summer | MK | -73.30 | 10.36 | 223.40 | -93.71 | -52.89 | 1 |
| Summer | LK | -76.55 | 10.36 | 223.40 | -96.96 | -56.13 | 1 |
| Fall | BB | 4.07 | 11.74 | 169.57 | -19.11 | 27.24 | 34 |
| Fall | UK | 0.63 | 12.34 | 202.49 | -23.71 | 24.97 | 234 |
| Fall | MK | 7.17 | 9.37 | 156.40 | -11.33 | 25.67 | 4 |
| Fall | LK | -11.66 | 8.98 | 133.90 | -29.42 | 6.10 | 234 |
| Inshore | |||||||
| Winter | BB | 40.69 | 12.34 | 202.54 | 16.35 | 65.03 | 67 |
| Winter | UK | 48.86 | 11.02 | 271.46 | 27.17 | 70.55 | 67 |
| Winter | MK | 71.49 | 10.56 | 238.06 | 50.68 | 92.30 | 7 |
| Winter | LK | 7.31 | 9.67 | 172.74 | -11.77 | 26.39 | 56 |
| Spring | BB | -62.31 | 11.75 | 166.85 | -85.51 | -39.10 | 4 |
| Spring | UK | -56.82 | 10.36 | 223.40 | -77.23 | -36.41 | 4 |
| Spring | MK | -80.56 | 8.86 | 127.35 | -98.10 | -63.03 | 34 |
| Spring | LK | -124.04 | 8.75 | 121.46 | -141.36 | -106.72 | 23 |
| Summer | BB | -46.25 | 12.69 | 223.40 | -71.25 | -21.25 | 45 |
| Summer | UK | -35.23 | 10.36 | 223.40 | -55.65 | -14.82 | 45 |
| Summer | MK | -155.72 | 10.36 | 223.40 | -176.13 | -135.30 | 12 |
| Summer | LK | -195.69 | 10.36 | 223.40 | -216.10 | -175.28 | 1 |
| Fall | BB | 26.23 | 11.23 | 144.22 | 4.04 | 48.42 | 67 |
| Fall | UK | 44.42 | 10.36 | 223.40 | 24.01 | 64.83 | 67 |
| Fall | MK | 16.75 | 9.37 | 155.65 | -1.76 | 35.26 | 6 |
| Fall | LK | -63.22 | 8.98 | 133.90 | -80.98 | -45.46 | 4 |
dTA_year.emmc<-emmeans(dTA_season, ~Sub_region*Zone)
dTA_year.emmc<-multcomp::cld(dTA_year.emmc)
#dTA_ses.emmc<-as.data.frame(dTA_ses.emmc[complete.cases(dTA_ses.emmc),])
dTA_year.emmc<-dTA_year.emmc[order(dTA_year.emmc$Zone,
dTA_year.emmc$Sub_region), ]
#dTA_year.emmc
dTA_year.emmc[,-c(2)] %>% kable("html", digits=2, row.names = F,
caption = "Year dTA") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Offshore", 1, 4) %>% # groups rows with zone label
pack_rows(., "Mid-Channel", 5, 8) %>%
pack_rows(., "Inshore", 9, 12)| Sub_region | emmean | SE | df | lower.CL | upper.CL | .group |
|---|---|---|---|---|---|---|
| Offshore | ||||||
| BB | -2.20 | 6.87 | 21.17 | -16.47 | 12.08 | 34 |
| UK | -0.77 | 5.82 | 24.60 | -12.76 | 11.22 | 4 |
| MK | -7.10 | 5.56 | 20.63 | -18.68 | 4.48 | 34 |
| LK | -5.75 | 4.81 | 20.39 | -15.76 | 4.27 | 34 |
| Mid-Channel | ||||||
| BB | -2.46 | 7.03 | 23.41 | -16.99 | 12.08 | 34 |
| UK | 0.79 | 7.20 | 25.61 | -14.02 | 15.59 | 34 |
| MK | -19.44 | 5.56 | 20.64 | -31.02 | -7.86 | 234 |
| LK | -30.79 | 5.41 | 18.44 | -42.13 | -19.45 | 23 |
| Inshore | ||||||
| BB | -10.41 | 6.84 | 20.88 | -24.64 | 3.82 | 234 |
| UK | 0.31 | 5.90 | 26.11 | -11.82 | 12.43 | 4 |
| MK | -37.01 | 5.58 | 20.96 | -48.62 | -25.40 | 2 |
| LK | -93.91 | 5.43 | 18.72 | -105.29 | -82.53 | 1 |
Figure 4A: Model
dTA_ses_2.emmc<-as.data.frame(dTA_ses.emmc)
dTA_ye_3.emmc<-as.data.frame(dTA_year.emmc)
dTA_ye_3.emmc$Season<-"Mean"
dTA_all_4<-rbind(dTA_ses_2.emmc, dTA_ye_3.emmc)
dTA_all_4$Zone<-factor(dTA_all_4$Zone,
levels=c("Inshore" , "Mid channel", "Offshore"))
DeltaYearTA<- ggplot(dTA_all_4, aes(x=Season, y=emmean, fill=Sub_region)) +
MyTheme +
#facet_grid(Zone~., scales = "free_y")+
facet_grid(~Zone, scales="free" )+
geom_hline(yintercept = 0, color="gray")+
theme(axis.title.x = element_blank(),
legend.position = "bottom",
axis.text.x = element_text(angle = 0))+
scale_y_continuous(#limits = c(-350,340),
expand = c(0.03, 0.03),
#breaks = seq(-800, 800, 50),
name=expression(paste(Delta, "TA (", mu, "mol ", ~kg^-1, ")")))+
geom_bar(position = "dodge", color="black",
stat = "summary",
fun = "mean")+
geom_errorbar(aes(ymin=emmean-SE, ymax=emmean+SE), width=0.2,
position=position_dodge(.9))
DeltaYearTA
LMER - dTA Inshore
# Inshore dTA
hist(inshore.season.data$dTA_MY)
shapiro.test(inshore.season.data$dTA_MY) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: inshore.season.data$dTA_MY
## W = 0.96933, p-value = 2.109e-07 dTA_season_in<-lmer(dTA_MY~ Sub_region * Season +
(1|SiteID), data=inshore.season.data)
#summary(dTA_season_in)
av.dTA_season_in<-anova(dTA_season_in)
av.dTA_season_in ranova(dTA_season_in) #step(dTA_season_in)
plot(dTA_season_in)
par(mfrow=c(1,2))
qqnorm(resid(dTA_season_in))
acf(resid(dTA_season_in), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#summ(dTA_season_in, scale = TRUE)
#plot_summs(dTA_season_in)
dTA_ses_in.emmc<-emmeans(dTA_season_in, ~ Sub_region*Season)
dTA_ses_in.emmc<-multcomp::cld(dTA_ses_in.emmc)
#dTA_ses_in.emmc<-as.data.frame(dTA_ses_in.emmc[complete.cases(dTA_ses_in.emmc),])
dTA_ses_in.emmc<-dTA_ses_in.emmc[order(dTA_ses_in.emmc$Season,
dTA_ses_in.emmc$Sub_region), ]
dTA_ses_in.emmcLMER - DIC season
hist(season.data$DIC_umol_kg)
shapiro.test(season.data$DIC_umol_kg) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: season.data$DIC_umol_kg
## W = 0.90935, p-value < 2.2e-16 DIC_season_0<-lmer(DIC_umol_kg~ Zone * Sub_region * Season +
(1|SiteID), data=season.data)
#summary(DIC_season_0)
av.DIC_season<-(anova(DIC_season_0))
av.DIC_season ranova(DIC_season_0) #step(DIC_season_0)
plot(DIC_season_0)
par(mfrow=c(1,2))
qqnorm(resid(DIC_season_0))
acf(resid(DIC_season_0), main="acf(resid(DIC Season))")
par(mfrow=c(1,1))
#summ(av.TA_season, scale = TRUE)
#plot_summs(av.DIC_season)
DIC_se.emmc<-emmeans(DIC_season_0, ~Zone*Season, by="Sub_region")
DIC_seas.emmc<-multcomp::cld(DIC_se.emmc)
DIC_seas.emmc DIC_se.emmc2<-emmeans(DIC_season_0, ~Sub_region*Season, by="Zone")
DIC_seas.emmc2<-multcomp::cld(TA_se.emmc2)
DIC_seas.emmc2LMER - all dDIC - MODEL 14
# Inshore Omega
hist(season.data$dDIC_MY)
shapiro.test(season.data$dDIC_MY) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: season.data$dDIC_MY
## W = 0.87617, p-value < 2.2e-16 # dDIC_season<-lmer(dDIC_MY~ Zone + Sub_region + Season +
# Zone:Sub_region + Zone:Season + Sub_region:Season+
# (1|SiteID), data=season.data)
dDIC_season<-lmer(dDIC_MY~ Zone * Sub_region * Season +
(1|SiteID), data=season.data)
#summary(dDIC_season)
av.dDIC_season<-anova(dDIC_season)
av.dDIC_season ranova(dDIC_season) step(dDIC_season)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 50 -6105.6 12311
## (1 | SiteID) 0 49 -6107.6 12313 4.0645 1 0.04379 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Zone:Sub_region:Season 0 115256 6403.1 18 1116.2 2.6592 0.0001976
##
## Zone:Sub_region:Season ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## dDIC_MY ~ Zone * Sub_region * Season + (1 | SiteID) plot(dDIC_season)
par(mfrow=c(1,2))
qqnorm(resid(dDIC_season))
acf(resid(dDIC_season), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#summ(dDIC_season, scale = TRUE)
#plot_summs(dDIC_season)
dDIC_ses.emmc<-emmeans(dDIC_season, ~Season*Sub_region, by="Zone")
dDIC_ses.emmc<-multcomp::cld(dDIC_ses.emmc)
#dDIC_ses.emmc<-as.data.frame(dDIC_ses.emmc[complete.cases(dDIC_ses.emmc),])
dDIC_ses.emmc<-dDIC_ses.emmc[order(dDIC_ses.emmc$Zone,
dDIC_ses.emmc$Season,
dDIC_ses.emmc$Sub_region), ]
#dDIC_ses.emmc
dDIC_ses.emmc[,-c(3)] %>% kable("html", digits=2, row.names = F,
caption = "Seasonal dDIC") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Offshore", 1, 16) %>% # groups rows with zone label
pack_rows(., "Mid-Channel", 17, 32) %>%
pack_rows(., "Inshore", 33, 48)| Season | Sub_region | emmean | SE | df | lower.CL | upper.CL | .group |
|---|---|---|---|---|---|---|---|
| Offshore | |||||||
| Winter | BB | -3.29 | 12.87 | 162.21 | -28.71 | 22.13 | 1 |
| Winter | UK | -2.52 | 11.00 | 187.84 | -24.22 | 19.19 | 1 |
| Winter | MK | 5.75 | 10.60 | 167.29 | -15.18 | 26.68 | 1 |
| Winter | LK | 5.28 | 8.89 | 147.46 | -12.30 | 22.85 | 1 |
| Spring | BB | -2.60 | 12.30 | 135.18 | -26.92 | 21.72 | 1 |
| Spring | UK | -3.02 | 10.43 | 156.68 | -23.62 | 17.58 | 1 |
| Spring | MK | -16.19 | 9.68 | 119.88 | -35.35 | 2.98 | 1 |
| Spring | LK | -7.97 | 8.33 | 116.47 | -24.48 | 8.53 | 1 |
| Summer | BB | 5.88 | 13.21 | 178.41 | -20.19 | 31.95 | 1 |
| Summer | UK | -3.02 | 11.21 | 202.07 | -25.12 | 19.09 | 1 |
| Summer | MK | -20.44 | 10.79 | 178.41 | -41.72 | 0.85 | 1 |
| Summer | LK | 0.36 | 9.75 | 207.11 | -18.87 | 19.58 | 1 |
| Fall | BB | 3.58 | 12.03 | 125.68 | -20.22 | 27.37 | 1 |
| Fall | UK | 2.72 | 10.42 | 157.42 | -17.87 | 23.31 | 1 |
| Fall | MK | 0.39 | 9.68 | 119.88 | -18.78 | 19.56 | 1 |
| Fall | LK | 1.18 | 8.41 | 121.15 | -15.48 | 17.84 | 1 |
| Mid-Channel | |||||||
| Winter | BB | 8.37 | 13.21 | 178.41 | -17.70 | 34.44 | 34 |
| Winter | UK | -0.72 | 13.59 | 196.52 | -27.51 | 26.08 | 234 |
| Winter | MK | 22.28 | 10.60 | 167.29 | 1.35 | 43.21 | 34 |
| Winter | LK | 29.98 | 9.82 | 125.91 | 10.55 | 49.41 | 4 |
| Spring | BB | -24.36 | 13.22 | 177.04 | -50.46 | 1.73 | 1234 |
| Spring | UK | -31.40 | 12.87 | 162.18 | -56.82 | -5.98 | 123 |
| Spring | MK | -68.20 | 9.55 | 114.12 | -87.12 | -49.27 | 1 |
| Spring | LK | -57.02 | 9.32 | 103.91 | -75.50 | -38.53 | 12 |
| Summer | BB | 4.03 | 13.21 | 178.41 | -22.04 | 30.10 | 34 |
| Summer | UK | -3.04 | 14.01 | 216.44 | -30.66 | 24.58 | 234 |
| Summer | MK | -59.92 | 10.79 | 178.41 | -81.21 | -38.64 | 12 |
| Summer | LK | -57.81 | 11.20 | 203.50 | -79.90 | -35.72 | 12 |
| Fall | BB | 22.18 | 12.28 | 136.54 | -2.10 | 46.47 | 34 |
| Fall | UK | 15.76 | 12.87 | 162.18 | -9.66 | 41.18 | 34 |
| Fall | MK | 22.59 | 9.81 | 126.26 | 3.17 | 42.01 | 34 |
| Fall | LK | 5.03 | 9.43 | 108.90 | -13.67 | 23.73 | 34 |
| Inshore | |||||||
| Winter | BB | 44.75 | 12.87 | 162.22 | 19.33 | 70.17 | 345 |
| Winter | UK | 40.45 | 11.44 | 217.19 | 17.90 | 63.00 | 34 |
| Winter | MK | 66.54 | 10.99 | 190.14 | 44.86 | 88.21 | 45 |
| Winter | LK | 26.00 | 10.11 | 139.51 | 6.01 | 45.99 | 234 |
| Spring | BB | -92.98 | 12.30 | 135.20 | -117.30 | -68.66 | 1 |
| Spring | UK | -126.79 | 10.79 | 178.41 | -148.07 | -105.50 | 1 |
| Spring | MK | -132.74 | 9.32 | 103.91 | -151.23 | -114.26 | 1 |
| Spring | LK | -135.06 | 9.21 | 99.36 | -153.34 | -116.79 | 1 |
| Summer | BB | -10.42 | 13.21 | 178.41 | -36.49 | 15.65 | 23 |
| Summer | UK | -25.40 | 10.79 | 178.41 | -46.69 | -4.12 | 2 |
| Summer | MK | -130.46 | 10.79 | 178.41 | -151.75 | -109.18 | 1 |
| Summer | LK | -107.78 | 11.43 | 218.75 | -130.31 | -85.25 | 1 |
| Fall | BB | 73.32 | 11.78 | 116.93 | 49.99 | 96.65 | 45 |
| Fall | UK | 96.30 | 10.79 | 178.41 | 75.01 | 117.59 | 5 |
| Fall | MK | 50.21 | 9.82 | 125.89 | 30.78 | 69.64 | 45 |
| Fall | LK | -7.47 | 9.43 | 108.90 | -26.17 | 11.23 | 23 |
dDIC_year.emmc<-emmeans(dDIC_season, ~Sub_region*Zone)
dDIC_year.emmc<-multcomp::cld(dDIC_year.emmc)
#dTA_ses.emmc<-as.data.frame(dTA_ses.emmc[complete.cases(dTA_ses.emmc),])
dDIC_year.emmc<-dDIC_year.emmc[order(dDIC_year.emmc$Zone,
dDIC_year.emmc$Sub_region), ]
#dDIC_year.emmc
dDIC_year.emmc[,-c(2)] %>% kable("html", digits=2, row.names = F,
caption = "Year dDIC") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Offshore", 1, 4) %>% # groups rows with zone label
pack_rows(., "Mid-Channel", 5, 8) %>%
pack_rows(., "Inshore", 9, 12)| Sub_region | emmean | SE | df | lower.CL | upper.CL | .group |
|---|---|---|---|---|---|---|
| Offshore | ||||||
| BB | 0.89 | 7.61 | 21.01 | -14.93 | 16.71 | 3 |
| UK | -1.46 | 6.41 | 23.83 | -14.68 | 11.77 | 3 |
| MK | -7.62 | 6.17 | 20.53 | -20.46 | 5.22 | 23 |
| LK | -0.29 | 5.36 | 20.69 | -11.44 | 10.86 | 3 |
| Mid-Channel | ||||||
| BB | 2.55 | 7.76 | 22.84 | -13.51 | 18.61 | 3 |
| UK | -4.85 | 7.91 | 24.65 | -21.15 | 11.45 | 23 |
| MK | -20.81 | 6.17 | 20.54 | -33.66 | -7.97 | 23 |
| LK | -19.95 | 6.07 | 19.30 | -32.65 | -7.26 | 23 |
| Inshore | ||||||
| BB | 3.67 | 7.58 | 20.76 | -12.11 | 19.44 | 3 |
| UK | -3.86 | 6.48 | 25.04 | -17.21 | 9.49 | 3 |
| MK | -36.62 | 6.19 | 20.81 | -49.49 | -23.74 | 12 |
| LK | -56.08 | 6.12 | 19.88 | -68.85 | -43.31 | 1 |
Figure 4B: Model
dDIC_ses_2.emmc<-as.data.frame(dDIC_ses.emmc)
dDIC_ye_3.emmc<-as.data.frame(dDIC_year.emmc)
dDIC_ye_3.emmc$Season<-"Mean"
dDIC_all_4<-rbind(dDIC_ses_2.emmc, dDIC_ye_3.emmc)
dDIC_all_4$Zone<-factor(dDIC_all_4$Zone,
levels=c("Inshore" , "Mid channel", "Offshore"))
DeltaYearDIC<- ggplot(dDIC_all_4, aes(x=Season, y=emmean, fill=Sub_region)) +
MyTheme +
#facet_grid(Zone~., scales = "free_y")+
facet_grid(~Zone, scales="free" )+
geom_hline(yintercept = 0, color="gray")+
theme(axis.title.x = element_blank(),
legend.position = "bottom",
axis.text.x = element_text(angle = 0))+
scale_y_continuous(#limits = c(-350,340),
expand = c(0.03, 0.03),
#breaks = seq(-800, 800, 50),
name=expression(paste(Delta, "DIC (", mu, "mol ", ~kg^-1, ")")))+
geom_bar(position = "dodge", color="black",
stat = "summary",
fun = "mean")+
geom_errorbar(aes(ymin=emmean-SE, ymax=emmean+SE), width=0.2,
position=position_dodge(.9))
DeltaYearDIC
LMER - dDIC Inshore
# Inshore dDIC
hist(inshore.season.data$dDIC_MY)
shapiro.test(inshore.season.data$dDIC_MY) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: inshore.season.data$dDIC_MY
## W = 0.99355, p-value = 0.09112 dDIC_season_in<-lmer(dDIC_MY~ Sub_region * Season +
(1|SiteID), data=inshore.season.data)
#summary(dDIC_season_in)
av.dDIC_season_in<-anova(dDIC_season_in)
av.dDIC_season_in ranova(dDIC_season_in) #step(dDIC_season_in)
plot(dDIC_season_in)
par(mfrow=c(1,1))
qqnorm(resid(dDIC_season_in))
acf(resid(dDIC_season_in), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#summ(dDIC_season_in, scale = TRUE)
#plot_summs(dDIC_season_in)
dDIC_ses_in.emmc<-emmeans(dDIC_season_in, ~ Sub_region*Season)
dDIC_ses_in.emmc<-multcomp::cld(dDIC_ses_in.emmc)
#dDIC_ses_in.emmc<-as.data.frame(dDIC_ses_in.emmc[complete.cases(dDIC_ses_in.emmc),])
dDIC_ses_in.emmc<-dDIC_ses_in.emmc[order(dDIC_ses_in.emmc$Season,
dDIC_ses_in.emmc$Sub_region), ]
dDIC_ses_in.emmcLMER - all dpCO2
# Inshore Omega
hist(season.data$dpCO2_MY)
shapiro.test(season.data$dpCO2_MY) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: season.data$dpCO2_MY
## W = 0.91004, p-value < 2.2e-16 # dpCO2_season<-lmer(dpCO2_MY~ Zone + Sub_region + Season +
# Zone:Sub_region + Zone:Season + Sub_region:Season+
# (1|SiteID), data=season.data)
dpCO2_season<-lmer(dpCO2_MY~ Zone * Sub_region * Season +
(1|SiteID), data=season.data)
#summary(dpCO2_season)
av.dPCO2_season<-anova(dpCO2_season)
av.dPCO2_season ranova(dpCO2_season) step(dpCO2_season)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 50 -6199.1 12498
## (1 | SiteID) 0 49 -6201.7 12501 5.0745 1 0.02428 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value
## Zone:Sub_region:Season 1 83814 4656 18 1113.09 1.6110
## Zone:Sub_region 2 21144 3524 6 21.92 1.2075
## Zone:Season 0 643024 107171 6 1132.67 36.7311
## Sub_region:Season 0 76429 8492 9 1131.81 2.9105
## Pr(>F)
## Zone:Sub_region:Season 0.050486 .
## Zone:Sub_region 0.339593
## Zone:Season < 2.2e-16 ***
## Sub_region:Season 0.002061 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## dpCO2_MY ~ Zone + Sub_region + Season + (1 | SiteID) + Zone:Season + Sub_region:Season plot(dpCO2_season)
par(mfrow=c(1,2))
qqnorm(resid(dpCO2_season))
acf(resid(dpCO2_season), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#plot_summs(dpCO2_season)
dpCO2_ses.emmc<-emmeans(dpCO2_season, ~Zone*Sub_region*Season)
dpCO2_ses.emmc<-multcomp::cld(dpCO2_ses.emmc)
#dpCO2_ses.emmc<-as.data.frame(dpCO2_ses.emmc[complete.cases(dpCO2_ses.emmc),])
dpCO2_ses.emmc<-dpCO2_ses.emmc[order(dpCO2_ses.emmc$Zone, dpCO2_ses.emmc$Season), ]
dpCO2_ses.emmc #summ(dpCO2_season, scale = TRUE)LMER - dpCO2 Inshore
# Inshore dDIC
hist(inshore.season.data$dpCO2_MY)
shapiro.test(inshore.season.data$dpCO2_MY) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: inshore.season.data$dpCO2_MY
## W = 0.97502, p-value = 2.647e-06 dpCO2_season_in<-lmer(dpCO2_MY~ Sub_region * Season +
(1|SiteID), data=inshore.season.data)
#summary(dpCO2_season_in)
av.dPCO2_season_in<-anova(dpCO2_season_in)
av.dPCO2_season_in ranova(dpCO2_season_in) #step(dpCO2_season_in)
plot(dpCO2_season_in)
par(mfrow=c(1,2))
qqnorm(resid(dpCO2_season_in))
acf(resid(dpCO2_season_in), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#summ(dpCO2_season_in, scale = TRUE)
#plot_summs(dpCO2_season_in)
dpCO2_ses_in.emmc<-emmeans(dpCO2_season_in, ~ Sub_region*Season)
dpCO2_ses_in.emmc<-multcomp::cld(dpCO2_ses_in.emmc)
#dpCO2_ses_in.emmc<-as.data.frame(dpCO2_ses_in.emmc[complete.cases(dpCO2_ses_in.emmc),])
dpCO2_ses_in.emmc<-dpCO2_ses_in.emmc[order(dpCO2_ses_in.emmc$Season,
dpCO2_ses_in.emmc$Sub_region), ]
dpCO2_ses_in.emmcLMER - all Omega
# Inshore Omega
hist(season.data$Aragonite_Sat_W)
shapiro.test(season.data$Aragonite_Sat_W) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: season.data$Aragonite_Sat_W
## W = 0.97358, p-value = 5.782e-14 # Omega_season<-lmer(Aragonite_Sat_W~ Zone + Sub_region + Season +
# Zone:Sub_region + Zone:Season + Sub_region:Season+
# (1|SiteID), data=season.data)
Omega_season<-lmer(Aragonite_Sat_W~ Zone * Sub_region * Season +
(1|SiteID), data=season.data)
#summary(Omega_season)
av.Ome_season<-anova(Omega_season)
av.Ome_season ranova(Omega_season) #step(Omega_season)
plot(Omega_season)
par(mfrow=c(1,2))
qqnorm(resid(Omega_season))
acf(resid(Omega_season), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#plot_summs(Omega_season)
# Ara_ses.emmc<-emmeans(Omega_season, ~Zone*Sub_region*Season)
# Ara_ses.emmc<-multcomp::cld(Ara_ses.emmc)
# Ara_ses.emmc<-as.data.frame(Ara_ses.emmc[complete.cases(Ara_ses.emmc),])
# Ara_ses.emmc<-Ara_ses.emmc[order(Ara_ses.emmc$Zone, Ara_ses.emmc$Season), ]
# Ara_ses.emmc
Ara_ses.emmc<-emmeans(Omega_season, ~Zone*Season, by="Sub_region")
Ara_ses.emmc<-multcomp::cld(Ara_ses.emmc)
Ara_ses.emmc Ara_ses.emmc2<-emmeans(Omega_season, ~Sub_region*Season, by="Zone")
Ara_ses.emmc2<-multcomp::cld(Ara_ses.emmc2)
Ara_ses.emmc2LMER - all dOmega - MODEL 16
# Inshore Omega
hist(season.data$dOme_MY)
shapiro.test(season.data$dOme_MY) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: season.data$dOme_MY
## W = 0.95185, p-value < 2.2e-16 # Omega_season<-lmer(Aragonite_Sat_W~ Zone + Sub_region + Season +
# Zone:Sub_region + Zone:Season + Sub_region:Season+
# (1|SiteID), data=season.data)
dOmega_season<-lmer(dOme_MY~ Zone * Sub_region * Season +
(1|SiteID), data=season.data)
#summary(dOmega_season)
av.dOme_season<-anova(dOmega_season)
av.dOme_season ranova(dOmega_season) #step(dOmega_season)
plot(dOmega_season)
par(mfrow=c(1,2))
qqnorm(resid(dOmega_season))
acf(resid(dOmega_season), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#plot_summs(dOmega_season)
dAra_ses.emmc<-emmeans(dOmega_season, ~Zone*Season, by="Sub_region")
dAra_ses.emmc<-multcomp::cld(dAra_ses.emmc)
dAra_ses.emmcdAra_ses.emmc1<-emmeans(dOmega_season, ~Sub_region*Season, by="Zone")
dAra_ses.emmc1<-multcomp::cld(dAra_ses.emmc1)
dAra_ses.emmc1dAra_ses.emmc2<-emmeans(dOmega_season, ~Season*Sub_region, by="Zone")
dAra_ses.emmc2<-multcomp::cld(dAra_ses.emmc2)
#dTA_ses.emmc<-as.data.frame(dTA_ses.emmc[complete.cases(dTA_ses.emmc),])
dAra_ses.emmc2<-dAra_ses.emmc2[order(dAra_ses.emmc2$Zone,
dAra_ses.emmc2$Season,
dAra_ses.emmc2$Sub_region), ]
dAra_ses.emmc2[,-c(3)] %>% kable("html", digits=2, row.names = F,
caption = "Seasonal d Omega") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Offshore", 1, 16) %>% # groups rows with zone label
pack_rows(., "Mid-Channel", 17, 32) %>%
pack_rows(., "Inshore", 33, 48)| Season | Sub_region | emmean | SE | df | lower.CL | upper.CL | .group |
|---|---|---|---|---|---|---|---|
| Offshore | |||||||
| Winter | BB | 0.04 | 0.07 | 195.69 | -0.10 | 0.19 | 1 |
| Winter | UK | 0.06 | 0.06 | 226.05 | -0.06 | 0.19 | 1 |
| Winter | MK | -0.02 | 0.06 | 201.96 | -0.14 | 0.10 | 1 |
| Winter | LK | -0.05 | 0.05 | 193.83 | -0.15 | 0.06 | 1 |
| Spring | BB | -0.01 | 0.07 | 161.37 | -0.15 | 0.13 | 1 |
| Spring | UK | 0.01 | 0.06 | 188.48 | -0.11 | 0.13 | 1 |
| Spring | MK | 0.09 | 0.06 | 143.17 | -0.02 | 0.20 | 1 |
| Spring | LK | -0.04 | 0.05 | 138.59 | -0.14 | 0.05 | 1 |
| Summer | BB | -0.13 | 0.08 | 215.81 | -0.28 | 0.02 | 1 |
| Summer | UK | -0.03 | 0.07 | 243.89 | -0.16 | 0.10 | 1 |
| Summer | MK | -0.07 | 0.06 | 215.81 | -0.19 | 0.05 | 1 |
| Summer | LK | -0.15 | 0.06 | 250.72 | -0.26 | -0.04 | 1 |
| Fall | BB | -0.05 | 0.07 | 150.12 | -0.19 | 0.09 | 1 |
| Fall | UK | -0.04 | 0.06 | 189.78 | -0.16 | 0.08 | 1 |
| Fall | MK | -0.02 | 0.06 | 143.17 | -0.13 | 0.09 | 1 |
| Fall | LK | -0.06 | 0.05 | 144.59 | -0.15 | 0.04 | 1 |
| Mid-Channel | |||||||
| Winter | BB | -0.02 | 0.08 | 215.81 | -0.17 | 0.13 | 123456 |
| Winter | UK | 0.11 | 0.08 | 237.77 | -0.05 | 0.26 | 23456 |
| Winter | MK | 0.02 | 0.06 | 201.96 | -0.10 | 0.14 | 2345 |
| Winter | LK | -0.17 | 0.06 | 150.53 | -0.28 | -0.06 | 1234 |
| Spring | BB | 0.16 | 0.08 | 213.37 | 0.01 | 0.31 | 456 |
| Spring | UK | 0.30 | 0.07 | 195.64 | 0.15 | 0.44 | 56 |
| Spring | MK | 0.30 | 0.06 | 135.99 | 0.19 | 0.41 | 6 |
| Spring | LK | 0.06 | 0.05 | 123.24 | -0.05 | 0.16 | 3456 |
| Summer | BB | -0.21 | 0.08 | 215.81 | -0.36 | -0.06 | 123 |
| Summer | UK | -0.03 | 0.08 | 261.11 | -0.19 | 0.13 | 123456 |
| Summer | MK | -0.20 | 0.06 | 215.81 | -0.32 | -0.07 | 123 |
| Summer | LK | -0.32 | 0.07 | 246.30 | -0.45 | -0.19 | 1 |
| Fall | BB | -0.20 | 0.07 | 163.90 | -0.34 | -0.06 | 123 |
| Fall | UK | -0.17 | 0.07 | 195.64 | -0.31 | -0.02 | 1234 |
| Fall | MK | -0.17 | 0.06 | 151.19 | -0.28 | -0.05 | 1234 |
| Fall | LK | -0.19 | 0.05 | 129.53 | -0.30 | -0.08 | 12 |
| Inshore | |||||||
| Winter | BB | -0.04 | 0.07 | 195.69 | -0.19 | 0.11 | 678 |
| Winter | UK | 0.08 | 0.07 | 262.40 | -0.06 | 0.21 | 78 |
| Winter | MK | 0.07 | 0.06 | 230.00 | -0.06 | 0.20 | 78 |
| Winter | LK | -0.22 | 0.06 | 167.01 | -0.33 | -0.10 | 4567 |
| Spring | BB | 0.26 | 0.07 | 161.40 | 0.12 | 0.41 | 89 |
| Spring | UK | 0.67 | 0.06 | 215.81 | 0.55 | 0.79 | 0 |
| Spring | MK | 0.47 | 0.05 | 123.24 | 0.37 | 0.58 | 90 |
| Spring | LK | 0.03 | 0.05 | 117.56 | -0.08 | 0.13 | 78 |
| Summer | BB | -0.41 | 0.08 | 215.81 | -0.56 | -0.26 | 2345 |
| Summer | UK | -0.12 | 0.06 | 215.81 | -0.24 | 0.01 | 567 |
| Summer | MK | -0.42 | 0.06 | 215.81 | -0.54 | -0.29 | 2345 |
| Summer | LK | -0.86 | 0.07 | 265.06 | -0.99 | -0.73 | 1 |
| Fall | BB | -0.48 | 0.07 | 139.48 | -0.62 | -0.35 | 234 |
| Fall | UK | -0.52 | 0.06 | 215.81 | -0.65 | -0.40 | 23 |
| Fall | MK | -0.35 | 0.06 | 150.50 | -0.46 | -0.24 | 3456 |
| Fall | LK | -0.65 | 0.05 | 129.53 | -0.76 | -0.54 | 12 |
dOm_year.emmc<-emmeans(dOmega_season, ~Sub_region*Zone)
dOm_year.emmc<-multcomp::cld(dOm_year.emmc)
#dTA_ses.emmc<-as.data.frame(dTA_ses.emmc[complete.cases(dTA_ses.emmc),])
dOm_year.emmc<-dOm_year.emmc[order(dOm_year.emmc$Zone,
dOm_year.emmc$Sub_region), ]
#dDIC_year.emmc
dOm_year.emmc[,-c(2)] %>% kable("html", digits=2, row.names = F,
caption = "Year d Omega") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Offshore", 1, 4) %>% # groups rows with zone label
pack_rows(., "Mid-Channel", 5, 8) %>%
pack_rows(., "Inshore", 9, 12)| Sub_region | emmean | SE | df | lower.CL | upper.CL | .group |
|---|---|---|---|---|---|---|
| Offshore | ||||||
| BB | -0.04 | 0.04 | 20.95 | -0.12 | 0.05 | 23 |
| UK | 0.00 | 0.04 | 24.28 | -0.07 | 0.07 | 23 |
| MK | -0.01 | 0.03 | 20.42 | -0.08 | 0.07 | 23 |
| LK | -0.07 | 0.03 | 21.00 | -0.13 | -0.01 | 23 |
| Mid-Channel | ||||||
| BB | -0.07 | 0.04 | 23.12 | -0.16 | 0.02 | 23 |
| UK | 0.05 | 0.04 | 25.25 | -0.04 | 0.14 | 3 |
| MK | -0.01 | 0.03 | 20.43 | -0.08 | 0.06 | 23 |
| LK | -0.16 | 0.03 | 18.99 | -0.23 | -0.09 | 2 |
| Inshore | ||||||
| BB | -0.17 | 0.04 | 20.67 | -0.25 | -0.08 | 23 |
| UK | 0.03 | 0.04 | 25.73 | -0.05 | 0.10 | 3 |
| MK | -0.06 | 0.03 | 20.74 | -0.13 | 0.01 | 23 |
| LK | -0.42 | 0.03 | 19.66 | -0.50 | -0.35 | 1 |
Figure 4D: Model
dAra_ses_2.emmc<-as.data.frame(dAra_ses.emmc2)
dAra_ye_3.emmc<-as.data.frame(dOm_year.emmc)
dAra_ye_3.emmc$Season<-"Mean"
dAra_all_4<-rbind(dAra_ses_2.emmc, dAra_ye_3.emmc)
dAra_all_4$Zone<-factor(dDIC_all_4$Zone,
levels=c("Inshore" , "Mid channel", "Offshore"))
DeltaYearAra<- ggplot(dAra_all_4, aes(x=Season, y=emmean, fill=Sub_region)) +
MyTheme +
#facet_grid(Zone~., scales = "free_y")+
facet_grid(~Zone, scales="free" )+
geom_hline(yintercept = 0, color="gray")+
theme(axis.title.x = element_blank(),
legend.position = "bottom",
axis.text.x = element_text(angle = 0))+
scale_y_continuous(#limits = c(-350,340),
expand = c(0.03, 0.03),
breaks = seq(-6, 6, 0.3),
name=(expression (paste(Delta ,
~Omega[Ar]))))+
geom_bar(position = "dodge", color="black",
stat = "summary",
fun = "mean")+
geom_errorbar(aes(ymin=emmean-SE, ymax=emmean+SE), width=0.2,
position=position_dodge(.9))
DeltaYearAra
LMER - Omega Inshore
# Inshore dOmega
hist(inshore.season.data$Aragonite_Sat_W)
shapiro.test(inshore.season.data$Aragonite_Sat_W) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: inshore.season.data$Aragonite_Sat_W
## W = 0.9918, p-value = 0.02632 Omega_season_in<-lmer(Aragonite_Sat_W~ Sub_region * Season +
(1|SiteID), data=inshore.season.data)
#summary(Omega_season_in)
av.Ome_season_in<-anova(Omega_season_in)
av.Ome_season_in ranova(Omega_season_in) #step(Omega_season_in)
plot(Omega_season_in)
par(mfrow=c(1,2))
qqnorm(resid(Omega_season_in))
acf(resid(Omega_season_in), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#summ(dDIC_season_in, scale = TRUE)
#plot_summs(Omega_season_in)
omega_ses_in.emmc<-emmeans(Omega_season_in, ~ Sub_region*Season)
omega_ses_in.emmc<-multcomp::cld(omega_ses_in.emmc)
#omega_ses_in.emmc<-as.data.frame(omega_ses_in.emmc[complete.cases(omega_ses_in.emmc),])
omega_ses_in.emmc<-omega_ses_in.emmc[order(omega_ses_in.emmc$Season,
omega_ses_in.emmc$Sub_region), ]
omega_ses_in.emmcLMER - all pH
# Inshore Omega
hist(season.data$pH_calculated)
shapiro.test(season.data$pH_calculated) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: season.data$pH_calculated
## W = 0.97272, p-value = 3.157e-14 # pH_season<-lmer(pH_calculated~ Zone + Sub_region + Season +
# Zone:Sub_region + Zone:Season + Sub_region:Season+
# (1|SiteID), data=season.data)
pH_season<-lmer(pH_calculated~ Zone * Sub_region * Season *
(1|SiteID), data=season.data)
#summary(pH_season)
av.pH_season<-anova(pH_season)
av.pH_season ranova(pH_season) #step(pH_season)
plot(pH_season)
par(mfrow=c(1,2))
qqnorm(resid(pH_season))
acf(resid(pH_season), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#summ(pH_season, scale = TRUE)
#plot_summs(pH_season)
pH_ses.emmc<-emmeans(pH_season, ~Zone*Sub_region*Season)
pH_ses.emmc<-multcomp::cld(pH_ses.emmc)
pH_ses.emmc<-as.data.frame(pH_ses.emmc[complete.cases(pH_ses.emmc),])
pH_ses.emmc<-pH_ses.emmc[order(pH_ses.emmc$Zone, pH_ses.emmc$Season), ]
pH_ses.emmc pH_ses.emmc<-emmeans(pH_season, ~Zone, by = "Sub_region")
pH_ses.emmc<-multcomp::cld(pH_ses.emmc)
pH_ses.emmc<-as.data.frame(pH_ses.emmc[complete.cases(pH_ses.emmc),])
#pH_ses.emmc<-pH_ses.emmc[order(pH_ses.emmc$Zone, pH_ses.emmc$Season), ]
pH_ses.emmc pH_ses.emmc<-emmeans(pH_season, ~Zone)
pH_ses.emmc<-multcomp::cld(pH_ses.emmc)
pH_ses.emmc<-as.data.frame(pH_ses.emmc[complete.cases(pH_ses.emmc),])
#pH_ses.emmc<-pH_ses.emmc[order(pH_ses.emmc$Zone, pH_ses.emmc$Season), ]
pH_ses.emmcLMER - all dpH - MODEL 15
# Inshore Omega
hist(season.data$dpH_MY)
shapiro.test(season.data$dpH_MY) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: season.data$dpH_MY
## W = 0.94163, p-value < 2.2e-16 # dpH_season<-lmer(Aragonite_Sat_W~ Zone + Sub_region + Season +
# Zone:Sub_region + Zone:Season + Sub_region:Season+
# (1|SiteID), data=season.data)
dpH_season<-lmer(dpH_MY~ Zone * Sub_region * Season +
(1|SiteID), data=season.data)
#summary(dpH_season)
av.dpH_season<-anova(dpH_season)
av.dpH_season ranova(dpH_season) #step(dpH_season)
plot(dpH_season)
par(mfrow=c(1,2))
qqnorm(resid(dpH_season))
acf(resid(dpH_season), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#plot_summs(dpH_season)
dpH_ses.emmc<-emmeans(dpH_season, ~Zone*Season, by="Sub_region")
dpH_ses.emmc<-multcomp::cld(dpH_ses.emmc)
dpH_ses.emmcdpH_ses.emmc<-emmeans(dpH_season, ~Sub_region)
dpH_ses.emmc<-multcomp::cld(dpH_ses.emmc)
dpH_ses.emmcdpH_ses.emmc2<-emmeans(dpH_season, ~Season*Sub_region, by="Zone")
dpH_ses.emmc2<-multcomp::cld(dpH_ses.emmc2)
#dTA_ses.emmc<-as.data.frame(dTA_ses.emmc[complete.cases(dTA_ses.emmc),])
dpH_ses.emmc2<-dpH_ses.emmc2[order(dpH_ses.emmc2$Zone,
dpH_ses.emmc2$Season,
dpH_ses.emmc2$Sub_region), ]
#dpH_ses.emmc2
dpH_ses.emmc2[,-c(3)] %>% kable("html", digits=2, row.names = F,
caption = "Seasonal d pH") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Offshore", 1, 16) %>% # groups rows with zone label
pack_rows(., "Mid-Channel", 17, 32) %>%
pack_rows(., "Inshore", 33, 48)| Season | Sub_region | emmean | SE | df | lower.CL | upper.CL | .group |
|---|---|---|---|---|---|---|---|
| Offshore | |||||||
| Winter | BB | 0.01 | 0.01 | 151.84 | -0.01 | 0.03 | 1 |
| Winter | UK | 0.01 | 0.01 | 175.85 | -0.01 | 0.03 | 1 |
| Winter | MK | 0.01 | 0.01 | 156.54 | -0.01 | 0.03 | 1 |
| Winter | LK | 0.00 | 0.01 | 151.04 | -0.01 | 0.02 | 1 |
| Spring | BB | 0.00 | 0.01 | 127.01 | -0.02 | 0.02 | 1 |
| Spring | UK | 0.00 | 0.01 | 146.80 | -0.01 | 0.02 | 1 |
| Spring | MK | 0.02 | 0.01 | 112.68 | 0.00 | 0.03 | 1 |
| Spring | LK | 0.00 | 0.01 | 109.59 | -0.02 | 0.01 | 1 |
| Summer | BB | -0.02 | 0.01 | 166.81 | -0.04 | 0.00 | 1 |
| Summer | UK | -0.01 | 0.01 | 188.96 | -0.03 | 0.01 | 1 |
| Summer | MK | -0.01 | 0.01 | 166.81 | -0.03 | 0.01 | 1 |
| Summer | LK | -0.02 | 0.01 | 193.49 | -0.04 | 0.00 | 1 |
| Fall | BB | 0.00 | 0.01 | 118.10 | -0.03 | 0.02 | 1 |
| Fall | UK | 0.00 | 0.01 | 147.40 | -0.02 | 0.02 | 1 |
| Fall | MK | 0.00 | 0.01 | 112.68 | -0.02 | 0.02 | 1 |
| Fall | LK | 0.00 | 0.01 | 113.88 | -0.02 | 0.01 | 1 |
| Mid-Channel | |||||||
| Winter | BB | 0.01 | 0.01 | 166.81 | -0.01 | 0.04 | 123456 |
| Winter | UK | 0.03 | 0.01 | 183.66 | 0.01 | 0.05 | 456 |
| Winter | MK | 0.02 | 0.01 | 156.54 | 0.00 | 0.04 | 3456 |
| Winter | LK | -0.01 | 0.01 | 118.29 | -0.03 | 0.01 | 12345 |
| Spring | BB | 0.03 | 0.01 | 165.71 | 0.01 | 0.05 | 456 |
| Spring | UK | 0.04 | 0.01 | 151.81 | 0.02 | 0.06 | 56 |
| Spring | MK | 0.04 | 0.01 | 107.36 | 0.03 | 0.06 | 6 |
| Spring | LK | 0.01 | 0.01 | 97.93 | 0.00 | 0.03 | 23456 |
| Summer | BB | -0.03 | 0.01 | 166.81 | -0.05 | -0.01 | 123 |
| Summer | UK | -0.01 | 0.01 | 202.38 | -0.04 | 0.01 | 12345 |
| Summer | MK | -0.03 | 0.01 | 166.81 | -0.05 | -0.01 | 12 |
| Summer | LK | -0.04 | 0.01 | 190.14 | -0.06 | -0.02 | 1 |
| Fall | BB | -0.02 | 0.01 | 128.09 | -0.04 | 0.00 | 1234 |
| Fall | UK | -0.01 | 0.01 | 151.81 | -0.04 | 0.01 | 1234 |
| Fall | MK | -0.01 | 0.01 | 118.56 | -0.03 | 0.01 | 12345 |
| Fall | LK | -0.02 | 0.01 | 102.53 | -0.03 | 0.00 | 1234 |
| Inshore | |||||||
| Winter | BB | 0.02 | 0.01 | 151.85 | 0.00 | 0.04 | 456 |
| Winter | UK | 0.04 | 0.01 | 203.00 | 0.02 | 0.06 | 567 |
| Winter | MK | 0.03 | 0.01 | 177.73 | 0.01 | 0.05 | 56 |
| Winter | LK | -0.01 | 0.01 | 130.95 | -0.02 | 0.01 | 345 |
| Spring | BB | 0.05 | 0.01 | 127.03 | 0.03 | 0.07 | 678 |
| Spring | UK | 0.10 | 0.01 | 166.81 | 0.08 | 0.12 | 8 |
| Spring | MK | 0.07 | 0.01 | 97.93 | 0.06 | 0.09 | 78 |
| Spring | LK | 0.02 | 0.01 | 93.73 | 0.01 | 0.04 | 56 |
| Summer | BB | -0.06 | 0.01 | 166.81 | -0.08 | -0.04 | 12 |
| Summer | UK | -0.03 | 0.01 | 166.81 | -0.05 | -0.01 | 234 |
| Summer | MK | -0.05 | 0.01 | 166.81 | -0.07 | -0.03 | 23 |
| Summer | LK | -0.11 | 0.01 | 204.28 | -0.13 | -0.09 | 1 |
| Fall | BB | -0.05 | 0.01 | 109.95 | -0.07 | -0.03 | 23 |
| Fall | UK | -0.06 | 0.01 | 166.81 | -0.08 | -0.04 | 2 |
| Fall | MK | -0.03 | 0.01 | 118.27 | -0.05 | -0.01 | 234 |
| Fall | LK | -0.06 | 0.01 | 102.53 | -0.07 | -0.04 | 2 |
dpH_year.emmc<-emmeans(dpH_season, ~Sub_region*Zone)
dpH_year.emmc<-multcomp::cld(dpH_year.emmc)
#dTA_ses.emmc<-as.data.frame(dTA_ses.emmc[complete.cases(dTA_ses.emmc),])
dpH_year.emmc<-dpH_year.emmc[order(dpH_year.emmc$Zone,
dpH_year.emmc$Sub_region), ]
#dDIC_year.emmc
dpH_year.emmc[,-c(2)] %>% kable("html", digits=2, row.names = F,
caption = "Year d pH") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Offshore", 1, 4) %>% # groups rows with zone label
pack_rows(., "Mid-Channel", 5, 8) %>%
pack_rows(., "Inshore", 9, 12)| Sub_region | emmean | SE | df | lower.CL | upper.CL | .group |
|---|---|---|---|---|---|---|
| Offshore | ||||||
| BB | 0.00 | 0.01 | 20.97 | -0.02 | 0.01 | 2 |
| UK | 0.00 | 0.01 | 23.62 | -0.01 | 0.01 | 2 |
| MK | 0.00 | 0.01 | 20.50 | -0.01 | 0.02 | 2 |
| LK | -0.01 | 0.00 | 21.00 | -0.02 | 0.00 | 2 |
| Mid-Channel | ||||||
| BB | 0.00 | 0.01 | 22.69 | -0.02 | 0.01 | 2 |
| UK | 0.01 | 0.01 | 24.39 | 0.00 | 0.03 | 2 |
| MK | 0.01 | 0.01 | 20.51 | -0.01 | 0.02 | 2 |
| LK | -0.01 | 0.01 | 19.34 | -0.03 | 0.00 | 12 |
| Inshore | ||||||
| BB | -0.01 | 0.01 | 20.73 | -0.02 | 0.00 | 12 |
| UK | 0.01 | 0.01 | 24.76 | 0.00 | 0.02 | 2 |
| MK | 0.01 | 0.01 | 20.76 | 0.00 | 0.02 | 2 |
| LK | -0.04 | 0.01 | 19.89 | -0.05 | -0.03 | 1 |
Figure 4C: Model
dpH_ses_2.emmc<-as.data.frame(dpH_ses.emmc2)
dpH_ye_3.emmc<-as.data.frame(dpH_year.emmc)
dpH_ye_3.emmc$Season<-"Mean"
dpH_all_4<-rbind(dpH_ses_2.emmc, dpH_ye_3.emmc)
dpH_all_4$Zone<-factor(dDIC_all_4$Zone,
levels=c("Inshore" , "Mid channel", "Offshore"))
DeltaYearpH<- ggplot(dpH_all_4, aes(x=Season, y=emmean, fill=Sub_region)) +
MyTheme +
#facet_grid(Zone~., scales = "free_y")+
facet_grid(~Zone, scales="free" )+
geom_hline(yintercept = 0, color="gray")+
theme(axis.title.x = element_blank(),
legend.position = "bottom",
axis.text.x = element_text(angle = 0))+
scale_y_continuous(#limits = c(-350,340),
expand = c(0.01, 0.01),
breaks = seq(-6, 6, 0.05),
name=(expression (paste(Delta ,
~pH[]))))+
geom_bar(position = "dodge", color="black",
stat = "summary",
fun = "mean")+
geom_errorbar(aes(ymin=emmean-SE, ymax=emmean+SE), width=0.2,
position=position_dodge(.9))
DeltaYearpH
LMER - pH Inshore
# Inshore pH
hist(inshore.season.data$pH_calculated)
shapiro.test(inshore.season.data$pH_calculated) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: inshore.season.data$pH_calculated
## W = 0.99362, p-value = 0.09003 pH_season_in<-lmer(pH_calculated~ Sub_region * Season +
(1|SiteID), data=inshore.season.data)
#summary(pH_season_in)
av.pH_season_in<-anova(pH_season_in)
av.pH_season_in ranova(pH_season_in) #step(pH_season_in)
plot(pH_season_in)
par(mfrow=c(1,2))
qqnorm(resid(pH_season_in))
acf(resid(pH_season_in), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#summ(pH_season_in, scale = TRUE)
#plot_summs(pH_season_in)
pH_ses_in.emmc<-emmeans(pH_season_in, ~ Sub_region*Season)
pH_ses_in.emmc<-multcomp::cld(pH_ses_in.emmc)
#pH_ses_in.emmc<-as.data.frame(pH_ses_in.emmc[complete.cases(pH_ses_in.emmc),])
pH_ses_in.emmc<-pH_ses_in.emmc[order(pH_ses_in.emmc$Season,
pH_ses_in.emmc$Sub_region), ]
pH_ses_in.emmcSummary all data
# ANOVAs
# Convert ANOVA results into dataframes allows for easier name manipulation
av.dTA_season <- data.frame(av.dTA_season)
av.dDIC_season <- data.frame(av.dDIC_season)
av.dPCO2_season <- data.frame(av.dPCO2_season)
av.dOme_season <- data.frame(av.dOme_season)
av.dpH_season <- data.frame(av.dpH_season)
# Putting all into one dataframe/table
anova_results_all <- data.frame(cbind
(c("Zone", "Sub_region", "Season", "Zone:Sub_region", "Zone:Season",
"Sub_region:Season", "Zone:Sub_region:Season",
"Zone", "Sub_region", "Season", "Zone:Sub_region", "Zone:Season",
"Sub_region:Season", "Zone:Sub_region:Season",
"Zone", "Sub_region", "Season", "Zone:Sub_region", "Zone:Season",
"Sub_region:Season", "Zone:Sub_region:Season",
"Zone", "Sub_region", "Season", "Zone:Sub_region", "Zone:Season",
"Sub_region:Season", "Zone:Sub_region:Season",
"Zone", "Sub_region", "Season", "Zone:Sub_region", "Zone:Season",
"Sub_region:Season", "Zone:Sub_region:Season"),
rbind(av.dTA_season, av.dDIC_season, av.dPCO2_season,
av.dOme_season, av.dpH_season )))
colnames(anova_results_all) <- c("", "Sum Sq", "Mean Sq", "N Df" , "D Df",
"F value", "Pr(>F)")
row.names(anova_results_all) <- NULL
# create HTML table using kableExtra
anova_results_all %>% kable("html", digits=2) %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "d TA", 1, 7) %>% # groups rows with label
pack_rows(., "d DIC", 8, 14) %>% # groups rows with label
pack_rows(., "d pCO2", 15, 21) %>% # groups rows with label
pack_rows(., "d Omega", 22, 28) %>% # groups rows with label
pack_rows(., "d pH", 29, 35) | Sum Sq | Mean Sq | N Df | D Df | F value | Pr(>F) | |
|---|---|---|---|---|---|---|
| d TA | ||||||
| Zone | 139072.57 | 69536.29 | 2 | 24.20 | 29.62 | 0.00 |
| Sub_region | 246323.49 | 82107.83 | 3 | 23.96 | 34.97 | 0.00 |
| Season | 908826.72 | 302942.24 | 3 | 1122.74 | 129.03 | 0.00 |
| Zone:Sub_region | 195949.68 | 32658.28 | 6 | 23.96 | 13.91 | 0.00 |
| Zone:Season | 620796.81 | 103466.13 | 6 | 1122.75 | 44.07 | 0.00 |
| Sub_region:Season | 184979.93 | 20553.33 | 9 | 1122.69 | 8.75 | 0.00 |
| Zone:Sub_region:Season | 104162.16 | 5786.79 | 18 | 1122.70 | 2.46 | 0.00 |
| d DIC | ||||||
| Zone | 50700.33 | 25350.16 | 2 | 23.61 | 10.53 | 0.00 |
| Sub_region | 88482.32 | 29494.11 | 3 | 23.46 | 12.25 | 0.00 |
| Season | 1412579.92 | 470859.97 | 3 | 1116.17 | 195.55 | 0.00 |
| Zone:Sub_region | 67333.95 | 11222.33 | 6 | 23.46 | 4.66 | 0.00 |
| Zone:Season | 1041238.03 | 173539.67 | 6 | 1116.18 | 72.07 | 0.00 |
| Sub_region:Season | 193626.44 | 21514.05 | 9 | 1116.14 | 8.93 | 0.00 |
| Zone:Sub_region:Season | 115256.36 | 6403.13 | 18 | 1116.15 | 2.66 | 0.00 |
| d pCO2 | ||||||
| Zone | 21292.59 | 10646.30 | 2 | 22.35 | 3.68 | 0.04 |
| Sub_region | 77914.85 | 25971.62 | 3 | 22.23 | 8.99 | 0.00 |
| Season | 1180943.62 | 393647.87 | 3 | 1113.12 | 136.19 | 0.00 |
| Zone:Sub_region | 25533.07 | 4255.51 | 6 | 22.23 | 1.47 | 0.23 |
| Zone:Season | 644262.40 | 107377.07 | 6 | 1113.12 | 37.15 | 0.00 |
| Sub_region:Season | 77749.05 | 8638.78 | 9 | 1113.08 | 2.99 | 0.00 |
| Zone:Sub_region:Season | 83813.71 | 4656.32 | 18 | 1113.09 | 1.61 | 0.05 |
| d Omega | ||||||
| Zone | 2.45 | 1.22 | 2 | 21.82 | 14.27 | 0.00 |
| Sub_region | 7.33 | 2.44 | 3 | 21.66 | 28.51 | 0.00 |
| Season | 37.55 | 12.52 | 3 | 1112.87 | 146.08 | 0.00 |
| Zone:Sub_region | 3.23 | 0.54 | 6 | 21.66 | 6.28 | 0.00 |
| Zone:Season | 20.93 | 3.49 | 6 | 1112.87 | 40.71 | 0.00 |
| Sub_region:Season | 2.65 | 0.29 | 9 | 1112.82 | 3.43 | 0.00 |
| Zone:Sub_region:Season | 2.10 | 0.12 | 18 | 1112.83 | 1.36 | 0.14 |
| d pH | ||||||
| Zone | 0.01 | 0.00 | 2 | 22.47 | 2.03 | 0.15 |
| Sub_region | 0.08 | 0.03 | 3 | 22.35 | 14.13 | 0.00 |
| Season | 0.82 | 0.27 | 3 | 1113.24 | 145.85 | 0.00 |
| Zone:Sub_region | 0.03 | 0.01 | 6 | 22.35 | 2.95 | 0.03 |
| Zone:Season | 0.41 | 0.07 | 6 | 1113.25 | 36.61 | 0.00 |
| Sub_region:Season | 0.04 | 0.00 | 9 | 1113.20 | 2.49 | 0.01 |
| Zone:Sub_region:Season | 0.04 | 0.00 | 18 | 1113.21 | 1.24 | 0.22 |
#export_summs(dTA_season, dDIC_season, dpCO2_season, scale = TRUE)
Seasonal_models<-list("d TA" = dTA_season,
"d DIC" = dDIC_season,
"d pCO2" = dpCO2_season,
"Omega" = Omega_season,
"pH" = pH_season)
modelsummary(Seasonal_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'Seasonal and spatial models'#,
#coef_map=factor_order
)| d TA | d DIC | d pCO2 | Omega | pH | |
|---|---|---|---|---|---|
| (Intercept) | 1.151 | −3.288 | −12.895 | 3.699*** | 8.074*** |
| (12.343) | (12.873) | (14.302) | (0.072) | (0.012) | |
| ZoneMid channel | 6.980 | 11.655 | 0.967 | −0.061 | −0.002 |
| (17.700) | (18.446) | (20.486) | (0.103) | (0.017) | |
| ZoneInshore | 39.538* | 48.038** | 4.090 | −0.082 | 0.006 |
| (17.456) | (18.206) | (20.227) | (0.102) | (0.017) | |
| Sub_regionUK | 2.872 | 0.772 | −2.000 | −0.009 | −0.003 |
| (16.251) | (16.932) | (18.803) | (0.095) | (0.016) | |
| Sub_regionMK | 3.553 | 9.035 | 5.679 | −0.057 | −0.004 |
| (15.992) | (16.676) | (18.526) | (0.093) | (0.015) | |
| Sub_regionLK | 0.894 | 8.565 | 9.487 | −0.110 | −0.010 |
| (15.119) | (15.645) | (17.520) | (0.088) | (0.015) | |
| SeasonSpring | −4.743 | 0.687 | 7.803 | 0.056 | −0.036* |
| (16.179) | (16.386) | (17.954) | (0.090) | (0.015) | |
| SeasonSummer | −6.663 | 9.168 | 38.495* | 0.178+ | −0.075*** |
| (16.878) | (17.093) | (18.728) | (0.096) | (0.016) | |
| SeasonFall | −1.989 | 6.863 | 17.605 | 0.059 | −0.035* |
| (15.985) | (16.188) | (17.736) | (0.091) | (0.015) | |
| ZoneMid channelSub_regionUK | −0.224 | −9.855 | −15.550 | 0.120 | 0.020 |
| (24.409) | (25.414) | (28.212) | (0.142) | (0.024) | |
| ZoneInshoreSub_regionUK | 5.297 | −5.072 | −18.754 | 0.104 | 0.020 |
| (23.191) | (24.150) | (26.813) | (0.135) | (0.022) | |
| ZoneMid channelSub_regionMK | 13.647 | 4.876 | −5.943 | 0.118 | 0.014 |
| (22.806) | (23.769) | (26.400) | (0.133) | (0.022) | |
| ZoneInshoreSub_regionMK | 27.249 | 12.751 | −10.947 | 0.171 | 0.015 |
| (22.797) | (23.761) | (26.390) | (0.133) | (0.022) | |
| ZoneMid channelSub_regionLK | 4.600 | 13.050 | 20.033 | −0.036 | −0.007 |
| (21.848) | (22.707) | (25.328) | (0.127) | (0.021) | |
| ZoneInshoreSub_regionLK | −34.273 | −27.317 | 13.701 | −0.070 | −0.011 |
| (21.780) | (22.642) | (25.258) | (0.127) | (0.021) | |
| ZoneMid channelSeasonSpring | −11.628 | −33.419 | −32.344 | 0.215+ | 0.031 |
| (23.567) | (23.869) | (26.152) | (0.130) | (0.022) | |
| ZoneInshoreSeasonSpring | −98.252*** | −138.416*** | −67.862** | 0.398** | 0.048* |
| (22.881) | (23.174) | (25.390) | (0.127) | (0.021) | |
| ZoneMid channelSeasonSummer | −15.249 | −13.510 | 7.990 | −0.021 | −0.007 |
| (24.049) | (24.355) | (26.684) | (0.136) | (0.023) | |
| ZoneInshoreSeasonSummer | −80.273*** | −64.334** | 47.962+ | −0.196 | −0.047* |
| (23.870) | (24.174) | (26.485) | (0.135) | (0.023) | |
| ZoneMid channelSeasonFall | −2.076 | 6.951 | 17.990 | −0.088 | −0.013 |
| (22.931) | (23.223) | (25.444) | (0.130) | (0.022) | |
| ZoneInshoreSeasonFall | −12.467 | 21.707 | 69.908** | −0.357** | −0.053* |
| (22.482) | (22.768) | (24.945) | (0.127) | (0.021) | |
| Sub_regionUKSeasonSpring | −1.044 | −1.189 | 1.093 | 0.044 | 0.004 |
| (21.323) | (21.599) | (23.667) | (0.119) | (0.020) | |
| Sub_regionMKSeasonSpring | −7.038 | −22.620 | −23.313 | 0.161 | 0.019 |
| (20.772) | (21.037) | (23.050) | (0.116) | (0.019) | |
| Sub_regionLKSeasonSpring | −8.531 | −13.936 | −3.961 | 0.079 | 0.003 |
| (19.669) | (19.823) | (21.827) | (0.110) | (0.018) | |
| Sub_regionUKSeasonSummer | −2.116 | −9.672 | −11.879 | 0.114 | 0.013 |
| (22.219) | (22.502) | (24.654) | (0.126) | (0.021) | |
| Sub_regionMKSeasonSummer | −22.759 | −35.351 | −25.654 | 0.119 | 0.015 |
| (21.832) | (22.110) | (24.225) | (0.124) | (0.021) | |
| Sub_regionLKSeasonSummer | −5.817 | −14.086 | −11.956 | 0.102 | 0.011 |
| (20.673) | (21.029) | (23.142) | (0.118) | (0.020) | |
| Sub_regionUKSeasonFall | −2.610 | −1.630 | −2.108 | 0.012 | 0.002 |
| (21.166) | (21.436) | (23.486) | (0.120) | (0.020) | |
| Sub_regionMKSeasonFall | −4.035 | −12.220 | −12.289 | 0.085 | 0.012 |
| (20.621) | (20.883) | (22.881) | (0.117) | (0.020) | |
| Sub_regionLKSeasonFall | −3.418 | −10.961 | −11.369 | 0.114 | 0.011 |
| (19.542) | (19.692) | (21.684) | (0.111) | (0.019) | |
| ZoneMid channelSub_regionUKSeasonSpring | 3.961 | 3.235 | 7.711 | 0.047 | −0.009 |
| (32.297) | (32.713) | (35.843) | (0.180) | (0.030) | |
| ZoneInshoreSub_regionUKSeasonSpring | −1.641 | −28.321 | −22.514 | 0.260 | 0.030 |
| (30.433) | (30.825) | (33.774) | (0.169) | (0.028) | |
| ZoneMid channelSub_regionMKSeasonSpring | −38.880 | −35.123 | 3.087 | −0.066 | −0.019 |
| (29.873) | (30.255) | (33.149) | (0.166) | (0.028) | |
| ZoneInshoreSub_regionMKSeasonSpring | −42.020 | −38.930 | 5.364 | −0.075 | −0.010 |
| (29.401) | (29.776) | (32.624) | (0.164) | (0.027) | |
| ZoneMid channelSub_regionLKSeasonSpring | −37.312 | −40.329 | −14.863 | −0.022 | −0.006 |
| (28.773) | (29.075) | (31.931) | (0.160) | (0.027) | |
| ZoneInshoreSub_regionLKSeasonSpring | −19.824 | −9.394 | 0.255 | −0.173 | −0.017 |
| (28.279) | (28.573) | (31.381) | (0.158) | (0.026) | |
| ZoneMid channelSub_regionUKSeasonSummer | 7.662 | 11.691 | 9.931 | −0.036 | −0.013 |
| (33.339) | (33.763) | (36.992) | (0.189) | (0.032) | |
| ZoneInshoreSub_regionUKSeasonSummer | 4.962 | −1.014 | −8.726 | 0.079 | −0.001 |
| (31.437) | (31.838) | (34.883) | (0.178) | (0.030) | |
| ZoneMid channelSub_regionMKSeasonSummer | −53.959+ | −42.507 | 10.706 | −0.163 | −0.025 |
| (31.014) | (31.409) | (34.412) | (0.176) | (0.029) | |
| ZoneInshoreSub_regionMKSeasonSummer | −117.512*** | −106.484*** | −21.498 | −0.239 | −0.012 |
| (31.007) | (31.402) | (34.405) | (0.176) | (0.029) | |
| ZoneMid channelSub_regionLKSeasonSummer | −62.444* | −69.360* | −17.010 | −0.053 | −0.005 |
| (29.950) | (30.545) | (33.537) | (0.171) | (0.029) | |
| ZoneInshoreSub_regionLKSeasonSummer | −110.248*** | −64.522* | 33.768 | −0.364* | −0.039 |
| (29.901) | (30.581) | (33.577) | (0.172) | (0.029) | |
| ZoneMid channelSub_regionUKSeasonFall | −3.477 | 4.291 | 14.555 | −0.107 | −0.017 |
| (31.827) | (32.232) | (35.315) | (0.180) | (0.030) | |
| ZoneInshoreSub_regionUKSeasonFall | 12.628 | 28.909 | 45.400 | −0.141 | −0.029 |
| (30.127) | (30.511) | (33.429) | (0.171) | (0.029) | |
| ZoneMid channelSub_regionMKSeasonFall | −10.064 | −1.282 | 4.943 | −0.117 | −0.013 |
| (29.458) | (29.833) | (32.686) | (0.167) | (0.028) | |
| ZoneInshoreSub_regionMKSeasonFall | −36.252 | −32.676 | −16.470 | −0.060 | 0.002 |
| (29.250) | (29.622) | (32.455) | (0.166) | (0.028) | |
| ZoneMid channelSub_regionLKSeasonFall | −17.802 | −27.804 | −22.804 | 0.047 | 0.010 |
| (28.314) | (28.606) | (31.417) | (0.161) | (0.027) | |
| ZoneInshoreSub_regionLKSeasonFall | −52.654+ | −51.078+ | −25.313 | −0.086 | 0.005 |
| (28.052) | (28.341) | (31.127) | (0.159) | (0.027) | |
| SD (Intercept SiteID) | 5.331 | 6.933 | 8.303 | 0.038 | 0.006 |
| SD (Observations) | 48.454 | 49.070 | 53.763 | 0.275 | 0.046 |
| Num.Obs. | 1188 | 1182 | 1180 | 1195 | 1195 |
| R2 Marg. | 0.504 | 0.537 | 0.385 | 0.452 | 0.459 |
| R2 Cond. | 0.510 | 0.546 | 0.399 | 0.462 | 0.468 |
| AIC | 12342.7 | 12311.2 | 12498.3 | 554.8 | −3543.0 |
| BIC | 12596.7 | 12565.0 | 12752.0 | 809.0 | −3288.7 |
| ICC | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| RMSE | 47.33 | 47.88 | 52.43 | 0.27 | 0.04 |
| + p |
#modelsummary(Omega_models, estimate = "p.value")Summary all inshore
# ANOVAs
# Convert ANOVA results into dataframes allows for easier name manipulation
av.dTA_season_in <- data.frame(av.dTA_season_in)
av.dDIC_season_in <- data.frame(av.dDIC_season_in)
av.dPCO2_season_in <- data.frame(av.dPCO2_season_in)
av.Ome_season_in <- data.frame(av.Ome_season_in)
av.pH_season_in <- data.frame(av.pH_season_in)
# Putting all into one dataframe/table
anova_results_In <- data.frame(cbind
(c("Sub_region", "Season", "Sub_region:Season",
"Sub_region", "Season", "Sub_region:Season",
"Sub_region", "Season", "Sub_region:Season",
"Sub_region", "Season", "Sub_region:Season",
"Sub_region", "Season", "Sub_region:Season"),
rbind(av.dTA_season_in, av.dDIC_season_in,
av.dPCO2_season_in,
av.Ome_season_in, av.pH_season_in)))
colnames(anova_results_In) <- c("", "Sum Sq", "Mean Sq", "N Df" , "D Df",
"F value", "Pr(>F)")
row.names(anova_results_In) <- NULL
# create HTML table using kableExtra
anova_results_In %>% kable("html", digits=2) %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "d TA inshore", 1, 3) %>% # groups rows with label
pack_rows(., "d DIC inshore", 4, 6) %>% # groups rows with label
pack_rows(., "d pCO2 inshore", 7, 9) %>% # groups rows with label
pack_rows(., "Omega inshore", 10, 12) %>% # groups rows with label
pack_rows(., "pH inshore", 13, 15) | Sum Sq | Mean Sq | N Df | D Df | F value | Pr(>F) | |
|---|---|---|---|---|---|---|
| d TA inshore | ||||||
| Sub_region | 423947.51 | 141315.84 | 3 | 8.18 | 27.43 | 0.00 |
| Season | 1367843.07 | 455947.69 | 3 | 375.41 | 88.51 | 0.00 |
| Sub_region:Season | 211905.70 | 23545.08 | 9 | 375.40 | 4.57 | 0.00 |
| d DIC inshore | ||||||
| Sub_region | 125627.20 | 41875.73 | 3 | 7.77 | 7.82 | 0.01 |
| Season | 2195416.50 | 731805.50 | 3 | 371.98 | 136.63 | 0.00 |
| Sub_region:Season | 213613.58 | 23734.84 | 9 | 371.97 | 4.43 | 0.00 |
| d pCO2 inshore | ||||||
| Sub_region | 65733.74 | 21911.25 | 3 | 7.29 | 3.61 | 0.07 |
| Season | 1552826.95 | 517608.98 | 3 | 371.52 | 85.23 | 0.00 |
| Sub_region:Season | 133286.22 | 14809.58 | 9 | 371.52 | 2.44 | 0.01 |
| Omega inshore | ||||||
| Sub_region | 6.21 | 2.07 | 3 | 7.08 | 14.66 | 0.00 |
| Season | 41.14 | 13.71 | 3 | 377.30 | 97.02 | 0.00 |
| Sub_region:Season | 3.88 | 0.43 | 9 | 377.30 | 3.05 | 0.00 |
| pH inshore | ||||||
| Sub_region | 0.08 | 0.03 | 3 | 7.35 | 6.50 | 0.02 |
| Season | 1.36 | 0.45 | 3 | 377.55 | 110.52 | 0.00 |
| Sub_region:Season | 0.07 | 0.01 | 9 | 377.55 | 1.98 | 0.04 |
#export_summs(dTA_season, dDIC_season, dpCO2_season, scale = TRUE)
Seasonal_models_in<-list("d TA" = dTA_season_in,
"d DIC" = dDIC_season_in,
"d pCO2" = dpCO2_season_in,
"Omega" = Omega_season_in,
"pH" = pH_season_in)
modelsummary(Seasonal_models_in, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'Seasonal and spatial models for inshore reefs'#,
#coef_map=factor_order
)| d TA | d DIC | d pCO2 | Omega | pH | |
|---|---|---|---|---|---|
| (Intercept) | 40.707* | 44.734* | −8.879 | 3.617*** | 8.080*** |
| (18.027) | (19.323) | (21.388) | (0.101) | (0.017) | |
| Sub_regionUK | 8.168 | −4.278 | −20.605 | 0.094 | 0.017 |
| (24.188) | (25.837) | (28.527) | (0.135) | (0.023) | |
| Sub_regionMK | 30.816 | 21.802 | −5.139 | 0.114 | 0.011 |
| (23.740) | (25.399) | (28.077) | (0.133) | (0.023) | |
| Sub_regionLK | −33.266 | −18.777 | 23.237 | −0.180 | −0.021 |
| (22.886) | (24.573) | (27.232) | (0.129) | (0.022) | |
| SeasonSpring | −102.964*** | −137.755*** | −60.185* | 0.455*** | 0.012 |
| (23.965) | (24.439) | (26.025) | (0.123) | (0.021) | |
| SeasonSummer | −86.954*** | −55.150* | 86.531** | −0.018 | −0.122*** |
| (25.001) | (25.493) | (27.147) | (0.131) | (0.022) | |
| SeasonFall | −14.460 | 28.573 | 87.527*** | −0.298* | −0.088*** |
| (23.416) | (23.877) | (25.425) | (0.123) | (0.021) | |
| Sub_regionUKSeasonSpring | −2.733 | −29.489 | −21.370 | 0.303+ | 0.034 |
| (32.162) | (32.799) | (34.928) | (0.164) | (0.028) | |
| Sub_regionMKSeasonSpring | −49.117 | −61.523+ | −17.865 | 0.085 | 0.009 |
| (30.819) | (31.428) | (33.467) | (0.159) | (0.027) | |
| Sub_regionLKSeasonSpring | −28.480 | −23.279 | −3.623 | −0.094 | −0.013 |
| (30.094) | (30.691) | (32.684) | (0.155) | (0.027) | |
| Sub_regionUKSeasonSummer | 2.846 | −10.707 | −20.754 | 0.194 | 0.012 |
| (32.941) | (33.592) | (35.772) | (0.173) | (0.029) | |
| Sub_regionMKSeasonSummer | −140.285*** | −141.851*** | −47.281 | −0.119 | 0.004 |
| (32.614) | (33.257) | (35.414) | (0.171) | (0.029) | |
| Sub_regionLKSeasonSummer | −116.177*** | −78.583* | 21.762 | −0.261 | −0.028 |
| (31.997) | (33.115) | (35.264) | (0.170) | (0.029) | |
| Sub_regionUKSeasonFall | 10.005 | 27.271 | 43.204 | −0.128 | −0.027 |
| (31.755) | (32.383) | (34.483) | (0.166) | (0.028) | |
| Sub_regionMKSeasonFall | −40.303 | −44.900 | −28.814 | 0.026 | 0.013 |
| (30.726) | (31.333) | (33.365) | (0.161) | (0.027) | |
| Sub_regionLKSeasonFall | −56.199+ | −62.002* | −36.671 | 0.028 | 0.015 |
| (29.809) | (30.398) | (32.371) | (0.156) | (0.027) | |
| SD (Intercept SiteID) | 6.619 | 10.789 | 14.147 | 0.063 | 0.010 |
| SD (Observations) | 71.772 | 73.184 | 77.929 | 0.376 | 0.064 |
| Num.Obs. | 397 | 394 | 394 | 400 | 400 |
| R2 Marg. | 0.520 | 0.560 | 0.422 | 0.501 | 0.484 |
| R2 Cond. | 0.524 | 0.569 | 0.440 | 0.514 | 0.497 |
| AIC | 4426.5 | 4409.1 | 4458.0 | 430.3 | −927.9 |
| BIC | 4498.2 | 4480.6 | 4529.6 | 502.2 | −856.0 |
| ICC | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| RMSE | 70.16 | 71.39 | 75.95 | 0.37 | 0.06 |
| + p |
#modelsummary(Omega_models, estimate = "p.value")Figure 4: Models
Figure_4<-ggarrange(DeltaYearTA+ theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
legend.position = "none"),
DeltaYearDIC+ theme(legend.position = "none",
axis.title.x=element_blank(),
axis.text.x=element_blank(),
strip.text.x = element_blank()),
DeltaYearpH + theme(legend.position = "none",
axis.title.x=element_blank(),
axis.text.x=element_blank(),
strip.text.x = element_blank()),
DeltaYearAra+ theme(legend.position = "bottom",
strip.text.x = element_blank(),
panel.spacing = unit(0.8, "lines")),
labels = c("A", "B", "C", "D"),
ncol = 1, nrow = 4, heights = c(1, 0.9, 0.9, 1.2))
Figure_4
#ggsave(file="FLK_results/Figures/Fig_4/Figure_4.svg", plot=Figure_4, dpi = 300, width=8, height=7)6 LMER long trend MODELS
The F statistic in a regression model is used to test whether there is a linear model of the data - overall , or equivalently that your model is no better than a model with just the intercept.
The t statistic for a coefficient in a linear model tests the H0 that the true coefficient is 0.
*The t-statistic is telling you about each coefficient individually. You can have a model with a low overall linear fit, but one individual coefficient may be relatively contributing a lot to explanation the variation in the DV that is explained on the assumption that there is a linear model.
Data and plots prep
Trends.data<-FLK.data_filtered[FLK.data_filtered$Year>2014, ]
#summary(Trends.data)
Reef.Trends.data<-WS.reef[WS.reef$Year>2014, ]
Reef.Trends.data<-Reef.Trends.data[Reef.Trends.data$Extreme=="Normal", ]
#summary(Reef.Trends.data$Extreme)
Trends.ocean<-Trends.data[Trends.data$Zone=="Oceanic", ]
Trends.offshore<-Reef.Trends.data[Reef.Trends.data$Zone=="Offshore", ]
Trends.midchanel<-Reef.Trends.data[Reef.Trends.data$Zone=="Mid channel", ]
Trends.inshore<-Reef.Trends.data[Reef.Trends.data$Zone=="Inshore", ]
Long_plot<- ggplot(Trends.data) +
MyTheme +
#scale_shape_manual(values=c(24, 25, 21))+
#guides(fill=guide_legend(override.aes=list(shape=21)))+
theme(axis.title.x = element_blank())+
scale_x_date(date_breaks = "2 years",
date_labels = "%Y",
expand = c(0.02, 0.02))
Long_plot2<- ggplot() +
MyTheme +
#scale_shape_manual(values=c(24, 25, 21))+
#guides(fill=guide_legend(override.aes=list(shape=21)))+
theme(axis.title.x = element_blank(),
axis.text.x = element_text(angle = 0, vjust = 0.5))+
scale_x_date(date_breaks = "2 years",
date_labels = "%Y",
expand = c(0.02, 0.02))
xs <- as.Date(as.character("2018-11-20"))
factor_order <- c('Year' = 'Year',
'(Intercept)' = 'Intercept',
'Sub_regionUK' = 'Upper Keys',
'Sub_regionMK' = 'Middle Keys',
'Sub_regionLK' = 'Lower Keys')Temperature-All
##
## Shapiro-Wilk normality test
##
## data: Trends.data$Aragonite_Sat_W
## W = 0.96676, p-value = 5.771e-16
## Model with all data
Temo_all_o<-lmer(Temperature_C~Year * Zone * Sub_region + (1|Month)+
(1|SiteID), data=Trends.data)
step(Temo_all_o)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 33 -1987.6 4041.3
## (1 | SiteID) 1 32 -1987.6 4039.3 0.0 1 1
## (1 | Month) 0 31 -3002.9 6067.9 2030.6 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Zone:Sub_region 1 4.4825 0.5603 8 1174.1 0.4030 0.9192438
## Zone:Sub_region 2 5.5939 0.6992 8 1182.0 0.5050 0.8531967
## Year:Sub_region 3 7.0805 2.3602 3 1190.1 1.7102 0.1630923
## Year:Zone 4 8.2475 2.7492 3 1193.0 1.9885 0.1139083
## Year 0 10.2765 10.2765 1 1199.3 7.4149 0.0065625
## Zone 0 26.2549 8.7516 3 1196.0 6.3146 0.0003002
## Sub_region 0 12.0698 4.0233 3 1196.0 2.9029 0.0338495
##
## Year:Zone:Sub_region
## Zone:Sub_region
## Year:Sub_region
## Year:Zone
## Year **
## Zone ***
## Sub_region *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## Temperature_C ~ Year + Zone + Sub_region + (1 | Month)Temp_all_1<-lmer(Temperature_C ~ Year + Zone + (1 | Month) +
Year:Zone, data=Trends.data)
anova(Temp_all_1, Temo_all_o) summary(Temp_all_1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Temperature_C ~ Year + Zone + (1 | Month) + Year:Zone
## Data: Trends.data
##
## REML criterion at convergence: 3942.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.4420 -0.5244 -0.0323 0.4733 3.4069
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 8.621 2.936
## Residual 1.389 1.179
## Number of obs: 1215, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.538e+01 6.689e+01 1.198e+03 0.529 0.5970
## Year -4.169e-03 3.315e-02 1.197e+03 -0.126 0.8999
## ZoneMid channel -1.385e+02 8.810e+01 1.196e+03 -1.572 0.1161
## ZoneOffshore -1.848e+02 8.383e+01 1.196e+03 -2.204 0.0277 *
## ZoneOceanic -2.060e+02 1.100e+02 1.196e+03 -1.874 0.0612 .
## Year:ZoneMid channel 6.877e-02 4.366e-02 1.196e+03 1.575 0.1155
## Year:ZoneOffshore 9.171e-02 4.155e-02 1.196e+03 2.207 0.0275 *
## Year:ZoneOceanic 1.023e-01 5.450e-02 1.196e+03 1.877 0.0607 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year ZnMdch ZnOffs ZnOcnc Yr:ZMc Yr:ZnOf
## Year -1.000
## ZoneMdchnnl -0.634 0.634
## ZoneOffshor -0.667 0.667 0.505
## ZoneOceanic -0.509 0.509 0.385 0.405
## Yr:ZnMdchnn 0.634 -0.634 -1.000 -0.505 -0.385
## Yr:ZnOffshr 0.667 -0.667 -0.505 -1.000 -0.405 0.505
## Year:ZnOcnc 0.509 -0.509 -0.385 -0.405 -1.000 0.385 0.405 anova(Temp_all_1) ranova(Temp_all_1)# Obtain slopes
#ranef(Temp_all_1)
#fixef(Temp_all_1)
#coef(Temp_all_1)$Month
#coef(Temp_all_1)$Zone
m.lst <- lstrends(Temp_all_1, "Zone", var="Year")
m.lst## Zone Year.trend SE df lower.CL upper.CL
## Inshore -0.00417 0.0332 1197 -0.06921 0.0609
## Mid channel 0.06460 0.0342 1197 -0.00255 0.1317
## Offshore 0.08754 0.0314 1198 0.02589 0.1492
## Oceanic 0.09814 0.0472 1197 0.00553 0.1908
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95 pairs(m.lst)## contrast estimate SE df t.ratio p.value
## Inshore - Mid channel -0.0688 0.0437 1196 -1.575 0.3933
## Inshore - Offshore -0.0917 0.0415 1196 -2.207 0.1218
## Inshore - Oceanic -0.1023 0.0545 1196 -1.877 0.2383
## Mid channel - Offshore -0.0229 0.0424 1196 -0.541 0.9490
## Mid channel - Oceanic -0.0335 0.0552 1196 -0.608 0.9295
## Offshore - Oceanic -0.0106 0.0535 1196 -0.198 0.9972
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 4 estimatesInshore
# Inshore Omega
hist(Trends.inshore$Temperature_C)
shapiro.test(Trends.inshore$Aragonite_Sat_W) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.inshore$Aragonite_Sat_W
## W = 0.99052, p-value = 0.02452 Temp_in_0<-lmer(Temperature_C~Year * Sub_region + (1|Month)+
#(1|Zone)+
(1|SiteID), data=Trends.inshore)
#summary(Omega_in_0)
#summary(Omega_in_1)
#anova(Omega_in_0, Omega_in_1)
#step(Omega_in_1)
anova(Temp_in_0) ranova(Temp_in_0) step(Temp_in_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -654.60 1331.2
## (1 | SiteID) 1 10 -654.60 1329.2 0.00 1 0.9999
## (1 | Month) 0 9 -933.69 1885.4 558.18 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 6.8641 2.28804 3 330.98 1.1298 0.3370
## Year 2 0.7414 0.74144 1 337.33 0.3657 0.5458
## Sub_region 3 7.9865 2.66217 3 335.00 1.3150 0.2694
##
## Model found:
## Temperature_C ~ (1 | Month) Temp_in<-lmer(Temperature_C ~ (1 | Month),
data=Trends.inshore)
ranova(Temp_in) summary(Temp_in)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Temperature_C ~ (1 | Month)
## Data: Trends.inshore
##
## REML criterion at convergence: 1299.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.1768 -0.4944 0.0139 0.5596 2.7909
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 12.05 3.471
## Residual 2.03 1.425
## Number of obs: 350, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 26.970 1.005 10.976 26.83 2.33e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 plot(Temp_in)
qqnorm(resid(Temp_in))
acf(resid(Temp_in), main="acf(resid(Temperature Inshore))")
Mid channel
hist(Trends.midchanel$Temperature_C)
shapiro.test(Trends.midchanel$Temperature_C)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.midchanel$Temperature_C
## W = 0.97341, p-value = 1.19e-05Temp_mc_0<-lmer(Temperature_C~Year * Sub_region + (1|Month) +
#(1|Zone)+
(1|SiteID), data=Trends.midchanel)
anova(Temp_mc_0)ranova(Temp_mc_0)step(Temp_mc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -507.18 1036.4
## (1 | SiteID) 1 10 -507.18 1034.4 0.00 1 1
## (1 | Month) 0 9 -794.85 1607.7 575.34 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 1.0153 0.3384 3 301.95 0.3102 0.81802
## Sub_region 2 4.3399 1.4466 3 304.98 1.3350 0.26306
## Year 3 3.2073 3.2073 1 310.48 2.9501 0.08687 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## Temperature_C ~ (1 | Month)Temp_mc<-lmer(Temperature_C ~ (1 | Month),
data = Trends.midchanel)
summary(Temp_mc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Temperature_C ~ (1 | Month)
## Data: Trends.midchanel
##
## REML criterion at convergence: 1001.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0547 -0.5463 -0.0556 0.5019 2.7801
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 8.549 2.924
## Residual 1.095 1.047
## Number of obs: 321, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 27.2421 0.8463 10.9775 32.19 3.23e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 plot(Temp_mc)
qqnorm(resid(Temp_mc))
acf(resid(Temp_mc), main="acf(resid(Omega mid Channel))")
Offshore
hist(Trends.offshore$Temperature_C)
shapiro.test(Trends.offshore$Temperature_C)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.offshore$Temperature_C
## W = 0.96684, p-value = 8.65e-08Temp_of_0<-lmer(Temperature_C~Year * Sub_region +
(1|Month) + (1|SiteID),
data=Trends.offshore)
#summary(Omega_of_0)
anova(Temp_of_0)ranova(Temp_of_0)step(Temp_of_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -490.60 1003.2
## (1 | SiteID) 1 10 -490.60 1001.2 0.00 1 1
## (1 | Month) 0 9 -906.96 1831.9 832.71 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 2.0849 0.6950 3 374.98 1.2340 0.297079
## Sub_region 2 3.1704 1.0568 3 377.98 1.8731 0.133631
## Year 0 4.4221 4.4221 1 382.67 7.7841 0.005535 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## Temperature_C ~ Year + (1 | Month)Temp_of<-lmer(Temperature_C ~ (1 | Month),
data =Trends.offshore)
summary(Temp_of)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Temperature_C ~ (1 | Month)
## Data: Trends.offshore
##
## REML criterion at convergence: 971.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2993 -0.6222 -0.0863 0.4675 3.2857
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 6.7781 2.6035
## Residual 0.5789 0.7608
## Number of obs: 394, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 27.2617 0.7527 10.9783 36.22 8.92e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 plot(Temp_of)
qqnorm(resid(Temp_of))
acf(resid(Temp_of), main="acf(resid(Omega offshore))") 
Oceanic
hist(Trends.ocean$Temperature_C)
shapiro.test(Trends.ocean$Temperature_C)# Normality NOT rejected##
## Shapiro-Wilk normality test
##
## data: Trends.ocean$Temperature_C
## W = 0.94994, p-value = 3.237e-05Temp_oc_0<-lmer(Temperature_C ~ Year * Sub_region +
(1 | Month)+ (1|SiteID),
data=Trends.ocean)
anova(Temp_oc_0) ranova(Temp_oc_0) step(Temp_oc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 9 -200.71 419.41
## (1 | SiteID) 1 8 -200.71 417.41 0.00 1 1
## (1 | Month) 0 7 -333.67 681.34 265.93 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 0.38013 0.19006 2 132.95 0.3494 0.7058
## Sub_region 2 0.10411 0.05206 2 134.95 0.0966 0.9079
## Year 3 0.58493 0.58493 1 138.67 1.1004 0.2960
##
## Model found:
## Temperature_C ~ (1 | Month)Temp_oc<-lmer(Temperature_C ~(1 | Month), data = Trends.ocean)
summary(Temp_oc)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Temperature_C ~ (1 | Month)
## Data: Trends.ocean
##
## REML criterion at convergence: 387.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2201 -0.5860 -0.1590 0.5806 2.9776
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 5.7367 2.3951
## Residual 0.5331 0.7301
## Number of obs: 150, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 27.3845 0.6943 10.9427 39.44 3.79e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ranova(Temp_oc) plot(Temp_oc)
qqnorm(resid(Temp_oc))
acf(resid(Temp_oc), main="acf(resid(Temp_oc open))")
Models summary
Complete models
# ANOVAs
# Convert ANOVA results into dataframes allows for easier name manipulation
av.Temp_in_0 <- data.frame (anova(Temp_in_0))
av.Temp_mc_0 <- data.frame (anova(Temp_mc_0) )
av.Temp_of_0 <- data.frame (anova(Temp_of_0))
av.Temp_oc_0 <- data.frame (anova(Temp_oc_0))
# Putting all into one dataframe/table
anova_Temp_0 <- data.frame(cbind
(c("Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region"),
rbind(av.Temp_in_0, av.Temp_mc_0,
av.Temp_of_0, av.Temp_oc_0)))
colnames(anova_Temp_0) <- c("", "Sum Sq", "Mean Sq", "N Df" , "D Df",
"F value", "Pr(>F)")
row.names(anova_Temp_0) <- NULL
# create HTML table using kableExtra
anova_Temp_0 %>% kable("html", digits=2,
caption="Initial temperature models fixed factors: Year*Region") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Omega inshore", 1, 3) %>% # groups rows with label
pack_rows(., "Omega mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "Omega offshore", 7, 9) %>% # groups rows with label
pack_rows(., "Omega ocean", 10, 12) | Sum Sq | Mean Sq | N Df | D Df | F value | Pr(>F) | |
|---|---|---|---|---|---|---|
| Omega inshore | ||||||
| Year | 1.11 | 1.11 | 1 | 334.28 | 0.55 | 0.46 |
| Sub_region | 6.86 | 2.29 | 3 | 331.01 | 1.13 | 0.34 |
| Year:Sub_region | 6.86 | 2.29 | 3 | 331.01 | 1.13 | 0.34 |
| Omega mid-channel | ||||||
| Year | 3.22 | 3.22 | 1 | 304.38 | 2.95 | 0.09 |
| Sub_region | 1.02 | 0.34 | 3 | 301.99 | 0.31 | 0.82 |
| Year:Sub_region | 1.02 | 0.34 | 3 | 301.99 | 0.31 | 0.82 |
| Omega offshore | ||||||
| Year | 5.35 | 5.35 | 1 | 376.55 | 9.51 | 0.00 |
| Sub_region | 2.08 | 0.69 | 3 | 374.95 | 1.23 | 0.30 |
| Year:Sub_region | 2.08 | 0.69 | 3 | 374.95 | 1.23 | 0.30 |
| Omega ocean | ||||||
| Year | 0.33 | 0.33 | 1 | 134.54 | 0.61 | 0.44 |
| Sub_region | 0.38 | 0.19 | 2 | 132.94 | 0.35 | 0.71 |
| Year:Sub_region | 0.38 | 0.19 | 2 | 132.94 | 0.35 | 0.71 |
# R-ANOVAs
rav.Temp_in_0 <- data.frame (ranova(Temp_in_0))
rav.Temp_mc_0 <- data.frame (ranova(Temp_mc_0) )
rav.Temp_of_0 <- data.frame (ranova(Temp_of_0))
rav.Temp_oc_0 <- data.frame (ranova(Temp_oc_0))
# Putting all into one dataframe/table
ranova_Temp_0 <- data.frame(cbind
(c("<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)"),
rbind(rav.Temp_in_0, rav.Temp_mc_0,
rav.Temp_of_0, rav.Temp_oc_0)))
colnames(ranova_Temp_0) <- c("", "npar", "logLik", "AIC" , "LRT",
"DF", "Pr(>Chi Sq)")
row.names(ranova_Temp_0) <- NULL
# create HTML table using kableExtra
ranova_Temp_0 %>% kable("html", digits=2,
caption="Initial temperature models random factors: Month+Station") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Omega inshore", 1, 3) %>% # groups rows with label
pack_rows(., "Omega mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "Omega offshore", 7, 9) %>% # groups rows with label
pack_rows(., "Omega ocean", 10, 12) | npar | logLik | AIC | LRT | DF | Pr(>Chi Sq) | |
|---|---|---|---|---|---|---|
| Omega inshore | ||||||
| <none> | 11 | -654.60 | 1331.20 | |||
| (1 | Month) | 10 | -933.69 | 1887.37 | 558.18 | 1 | 0 |
| (1 | SiteID) | 10 | -654.60 | 1329.20 | 0.00 | 1 | 1 |
| Omega mid-channel | ||||||
| <none> | 11 | -507.18 | 1036.36 | |||
| (1 | Month) | 10 | -794.85 | 1609.70 | 575.34 | 1 | 0 |
| (1 | SiteID) | 10 | -507.18 | 1034.36 | 0.00 | 1 | 1 |
| Omega offshore | ||||||
| <none> | 11 | -490.60 | 1003.21 | |||
| (1 | Month) | 10 | -906.96 | 1833.91 | 832.71 | 1 | 0 |
| (1 | SiteID) | 10 | -490.60 | 1001.21 | 0.00 | 1 | 1 |
| Omega ocean | ||||||
| <none> | 9 | -200.71 | 419.41 | |||
| (1 | Month) | 8 | -333.67 | 683.34 | 265.93 | 1 | 0 |
| (1 | SiteID) | 8 | -200.71 | 417.41 | 0.00 | 1 | 1 |
Temp_models_0<-list("Inshore" = Temp_in_0,
"Mid channel" = Temp_mc_0,
"Offshore" = Temp_of_0,
"Ocean" = Temp_oc_0)
modelsummary(Temp_models_0, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'Initial temperature model outputs (all factors)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | 0.107 | 0.091 | 0.133** | −0.008 |
| (0.088) | (0.066) | (0.047) | (0.071) | |
| Intercept | −189.516 | −157.210 | −241.064* | 44.111 |
| (177.456) | (132.483) | (94.175) | (143.502) | |
| Upper Keys | 52.599 | 86.321 | 133.838 | |
| (224.188) | (181.010) | (116.203) | ||
| Middle Keys | 171.794 | 12.996 | 134.123 | −95.121 |
| (217.328) | (160.231) | (115.108) | (161.560) | |
| Lower Keys | 355.325 | 128.530 | 209.830+ | −136.534 |
| (217.558) | (161.091) | (109.249) | (163.272) | |
| Num.Obs. | 350 | 321 | 394 | 150 |
| R2 Marg. | 0.022 | 0.030 | 0.053 | 0.018 |
| R2 Cond. | ||||
| AIC | 1331.2 | 1036.4 | 1003.2 | 419.4 |
| BIC | 1373.6 | 1077.8 | 1046.9 | 446.5 |
| RMSE | 1.38 | 1.01 | 0.73 | 0.69 |
| + p |
#modelsummary(Omega_models, estimate = "p.value")Models with non-significant factors removed
Temp_models<-list("Inshore" = Temp_in,
"Mid channel" = Temp_mc,
"Offshore" = Temp_of,
"Ocean" = Temp_oc)
modelsummary(Temp_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'Final Omega model outputs (dropped factors)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Intercept | 26.970*** | 27.242*** | 27.262*** | 27.385*** |
| (1.005) | (0.846) | (0.753) | (0.694) | |
| Num.Obs. | 350 | 321 | 394 | 150 |
| R2 Marg. | 0.000 | 0.000 | 0.000 | 0.000 |
| R2 Cond. | 0.856 | 0.886 | 0.921 | 0.915 |
| AIC | 1305.6 | 1007.2 | 977.3 | 393.5 |
| BIC | 1317.2 | 1018.5 | 989.2 | 402.5 |
| ICC | 0.9 | 0.9 | 0.9 | 0.9 |
| RMSE | 1.40 | 1.03 | 0.75 | 0.70 |
| + p |
#modelsummary(Temp_models, estimate = "p.value")DIC-All
##
## Shapiro-Wilk normality test
##
## data: Trends.data$DIC_umol_kg
## W = 0.88724, p-value < 2.2e-16
## Model with all data
DIC_all_o<-lmer(DIC_umol_kg~Year * Zone * Sub_region + (1|Month)+
(1|SiteID), data=Trends.data)
step(DIC_all_o)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 33 -6506.4 13079
## (1 | Month) 0 32 -6691.6 13447 370.46 1 < 2e-16 ***
## (1 | SiteID) 0 32 -6507.9 13080 2.92 1 0.08736 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Zone:Sub_region 1 11984 1498.0 8 1141.73 0.4676 0.87941
## Year:Zone 2 2028 676.1 3 1149.64 0.2118 0.88823
## Year:Sub_region 0 28636 9545.3 3 1152.78 2.9970 0.02983 *
## Zone:Sub_region 0 75555 9444.4 8 24.31 2.9653 0.01829 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## DIC_umol_kg ~ Year + Zone + Sub_region + (1 | Month) + (1 | SiteID) + Year:Sub_region + Zone:Sub_regionDIC_all_1<-lmer(DIC_umol_kg ~ Year + Zone + Sub_region +
(1 | Month) + (1 | SiteID) +
Year:Sub_region + Zone:Sub_region, data=Trends.data)
summary(DIC_all_1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DIC_umol_kg ~ Year + Zone + Sub_region + (1 | Month) + (1 | SiteID) +
## Year:Sub_region + Zone:Sub_region
## Data: Trends.data
##
## REML criterion at convergence: 13067.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5099 -0.4884 -0.0112 0.5678 6.5473
##
## Random effects:
## Groups Name Variance Std.Dev.
## SiteID (Intercept) 60.09 7.752
## Month (Intercept) 1578.89 39.735
## Residual 3184.94 56.435
## Number of obs: 1204, groups: SiteID, 38; Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3353.3045 4055.3416 1160.1473 0.827 0.408472
## Year -0.6446 2.0097 1160.1249 -0.321 0.748468
## ZoneMid channel -3.7666 12.5161 22.6641 -0.301 0.766204
## ZoneOffshore -3.2952 12.4859 22.4488 -0.264 0.794255
## ZoneOceanic 62.8678 11.6646 24.5997 5.390 1.44e-05
## Sub_regionUK 3869.6370 5098.3143 1153.1660 0.759 0.448006
## Sub_regionMK -7720.1396 4801.3446 1151.9184 -1.608 0.108128
## Sub_regionLK 2294.5076 4769.8674 1152.0710 0.481 0.630578
## Year:Sub_regionUK -1.9277 2.5267 1153.1592 -0.763 0.445656
## Year:Sub_regionMK 3.8024 2.3794 1151.9066 1.598 0.110305
## Year:Sub_regionLK -1.1709 2.3638 1152.0599 -0.495 0.620461
## ZoneMid channel:Sub_regionUK 11.0869 17.2789 24.3678 0.642 0.527096
## ZoneOffshore:Sub_regionUK 15.3582 16.3369 23.6156 0.940 0.356686
## ZoneOceanic:Sub_regionUK -52.7240 19.3741 27.6910 -2.721 0.011109
## ZoneMid channel:Sub_regionMK 25.0072 16.2207 23.0145 1.542 0.136790
## ZoneOffshore:Sub_regionMK 38.7518 16.1872 22.8264 2.394 0.025282
## ZoneOceanic:Sub_regionMK -18.6541 16.4642 24.4183 -1.133 0.268215
## ZoneMid channel:Sub_regionLK 42.4505 16.1786 22.7731 2.624 0.015247
## ZoneOffshore:Sub_regionLK 65.1182 15.7412 22.6121 4.137 0.000412
##
## (Intercept)
## Year
## ZoneMid channel
## ZoneOffshore
## ZoneOceanic ***
## Sub_regionUK
## Sub_regionMK
## Sub_regionLK
## Year:Sub_regionUK
## Year:Sub_regionMK
## Year:Sub_regionLK
## ZoneMid channel:Sub_regionUK
## ZoneOffshore:Sub_regionUK
## ZoneOceanic:Sub_regionUK *
## ZoneMid channel:Sub_regionMK
## ZoneOffshore:Sub_regionMK *
## ZoneOceanic:Sub_regionMK
## ZoneMid channel:Sub_regionLK *
## ZoneOffshore:Sub_regionLK ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficient anova(DIC_all_1) ranova(DIC_all_1)# Obtain slopes
# ranef(DIC_all_1)
# fixef(DIC_all_1)
# coef(DIC_all_1)$Month
# coef(DIC_all_1)$Zone
m.lst <- lstrends(DIC_all_1, "Sub_region", var="Year")
m.lst## Sub_region Year.trend SE df lower.CL upper.CL
## BB -0.645 2.01 1159 -4.591 3.302
## UK -2.572 1.77 1164 -6.049 0.904
## MK 3.158 1.55 1162 0.115 6.201
## LK -1.815 1.54 1163 -4.834 1.203
##
## Results are averaged over the levels of: Zone
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95 #pairs(m.lst)Inshore
# Inshore DIC
hist(Trends.inshore$DIC_umol_kg)
shapiro.test(Trends.inshore$DIC_umol_kg) # Normality NOT rejected##
## Shapiro-Wilk normality test
##
## data: Trends.inshore$DIC_umol_kg
## W = 0.99654, p-value = 0.66 DIC_in_0<-lmer(DIC_umol_kg~Year * Sub_region + (1|Month)+
#(1|Zone)+
(1|SiteID), data=Trends.inshore)
DIC_in_1<-lmer(DIC_umol_kg~Year * Sub_region * Season +
(1|Month)+
#(1|Zone)+
(1|SiteID), data=Trends.inshore)
summary(DIC_in_0)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DIC_umol_kg ~ Year * Sub_region + (1 | Month) + (1 | SiteID)
## Data: Trends.inshore
##
## REML criterion at convergence: 3951.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0197 -0.5938 -0.0006 0.6369 4.2110
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 7114.2 84.35
## SiteID (Intercept) 253.5 15.92
## Residual 5223.8 72.28
## Number of obs: 347, groups: Month, 12; SiteID, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5059.086 8991.623 325.116 0.563 0.574
## Year -1.489 4.456 325.110 -0.334 0.739
## Sub_regionUK 2656.414 11390.728 321.591 0.233 0.816
## Sub_regionMK -16751.972 11039.360 321.282 -1.517 0.130
## Sub_regionLK 2257.089 11122.331 321.380 0.203 0.839
## Year:Sub_regionUK -1.325 5.645 321.591 -0.235 0.815
## Year:Sub_regionMK 8.279 5.471 321.280 1.513 0.131
## Year:Sub_regionLK -1.154 5.512 321.378 -0.209 0.834
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK Sb_rLK Y:S_UK Y:S_MK
## Year -1.000
## Sub_reginUK -0.700 0.700
## Sub_reginMK -0.723 0.723 0.569
## Sub_reginLK -0.710 0.710 0.563 0.581
## Yr:Sb_rgnUK 0.700 -0.700 -1.000 -0.569 -0.563
## Yr:Sb_rgnMK 0.723 -0.723 -0.569 -1.000 -0.581 0.569
## Yr:Sb_rgnLK 0.710 -0.710 -0.563 -0.581 -1.000 0.563 0.581 summary(DIC_in_1) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DIC_umol_kg ~ Year * Sub_region * Season + (1 | Month) + (1 |
## SiteID)
## Data: Trends.inshore
##
## REML criterion at convergence: 3692.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0473 -0.5931 -0.0299 0.5879 4.0020
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 751.6 27.42
## SiteID (Intercept) 232.7 15.25
## Residual 4647.1 68.17
## Number of obs: 347, groups: Month, 12; SiteID, 11
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.816e+03 2.150e+04 2.888e+02 0.270
## Year -1.839e+00 1.066e+01 2.889e+02 -0.173
## Sub_regionUK -1.721e+04 2.721e+04 3.023e+02 -0.633
## Sub_regionMK -2.634e+04 2.626e+04 3.004e+02 -1.003
## Sub_regionLK 1.306e+04 2.617e+04 3.007e+02 0.499
## SeasonSpring -2.917e+04 2.637e+04 3.033e+02 -1.106
## SeasonSummer 1.524e+04 2.731e+04 3.013e+02 0.558
## SeasonFall 2.080e+04 2.714e+04 2.835e+02 0.766
## Year:Sub_regionUK 8.528e+00 1.348e+01 3.023e+02 0.632
## Year:Sub_regionMK 1.307e+01 1.302e+01 3.004e+02 1.004
## Year:Sub_regionLK -6.490e+00 1.297e+01 3.007e+02 -0.500
## Year:SeasonSpring 1.438e+01 1.307e+01 3.033e+02 1.101
## Year:SeasonSummer -7.596e+00 1.354e+01 3.013e+02 -0.561
## Year:SeasonFall -1.030e+01 1.345e+01 2.835e+02 -0.766
## Sub_regionUK:SeasonSpring 4.134e+04 3.346e+04 3.019e+02 1.235
## Sub_regionMK:SeasonSpring 4.831e+04 3.270e+04 3.006e+02 1.477
## Sub_regionLK:SeasonSpring -1.411e+04 3.287e+04 3.011e+02 -0.429
## Sub_regionUK:SeasonSummer 1.386e+03 3.441e+04 3.019e+02 0.040
## Sub_regionMK:SeasonSummer 6.958e+03 3.367e+04 3.007e+02 0.207
## Sub_regionLK:SeasonSummer -1.834e+04 3.463e+04 3.027e+02 -0.529
## Sub_regionUK:SeasonFall 3.896e+04 3.503e+04 3.021e+02 1.112
## Sub_regionMK:SeasonFall -1.939e+03 3.312e+04 3.007e+02 -0.059
## Sub_regionLK:SeasonFall -5.540e+03 3.287e+04 3.007e+02 -0.169
## Year:Sub_regionUK:SeasonSpring -2.051e+01 1.658e+01 3.019e+02 -1.237
## Year:Sub_regionMK:SeasonSpring -2.398e+01 1.620e+01 3.006e+02 -1.480
## Year:Sub_regionLK:SeasonSpring 6.980e+00 1.629e+01 3.011e+02 0.429
## Year:Sub_regionUK:SeasonSummer -6.894e-01 1.706e+01 3.019e+02 -0.040
## Year:Sub_regionMK:SeasonSummer -3.518e+00 1.669e+01 3.007e+02 -0.211
## Year:Sub_regionLK:SeasonSummer 9.052e+00 1.717e+01 3.027e+02 0.527
## Year:Sub_regionUK:SeasonFall -1.931e+01 1.736e+01 3.021e+02 -1.112
## Year:Sub_regionMK:SeasonFall 9.310e-01 1.641e+01 3.007e+02 0.057
## Year:Sub_regionLK:SeasonFall 2.715e+00 1.629e+01 3.007e+02 0.167
## Pr(>|t|)
## (Intercept) 0.787
## Year 0.863
## Sub_regionUK 0.527
## Sub_regionMK 0.317
## Sub_regionLK 0.618
## SeasonSpring 0.270
## SeasonSummer 0.577
## SeasonFall 0.444
## Year:Sub_regionUK 0.528
## Year:Sub_regionMK 0.316
## Year:Sub_regionLK 0.617
## Year:SeasonSpring 0.272
## Year:SeasonSummer 0.575
## Year:SeasonFall 0.445
## Sub_regionUK:SeasonSpring 0.218
## Sub_regionMK:SeasonSpring 0.141
## Sub_regionLK:SeasonSpring 0.668
## Sub_regionUK:SeasonSummer 0.968
## Sub_regionMK:SeasonSummer 0.836
## Sub_regionLK:SeasonSummer 0.597
## Sub_regionUK:SeasonFall 0.267
## Sub_regionMK:SeasonFall 0.953
## Sub_regionLK:SeasonFall 0.866
## Year:Sub_regionUK:SeasonSpring 0.217
## Year:Sub_regionMK:SeasonSpring 0.140
## Year:Sub_regionLK:SeasonSpring 0.669
## Year:Sub_regionUK:SeasonSummer 0.968
## Year:Sub_regionMK:SeasonSummer 0.833
## Year:Sub_regionLK:SeasonSummer 0.598
## Year:Sub_regionUK:SeasonFall 0.267
## Year:Sub_regionMK:SeasonFall 0.955
## Year:Sub_regionLK:SeasonFall 0.868 anova(DIC_in_0, DIC_in_1) step(DIC_in_1)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 35 -1846.2 3762.4
## (1 | Month) 0 34 -1856.0 3780.0 19.6316 1 9.39e-06 ***
## (1 | SiteID) 0 34 -1848.4 3764.8 4.3996 1 0.03595 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region:Season 1 53301 5922.3 9 302.13 1.2744 0.25011
## Year:Sub_region 2 13312 4437.5 3 311.20 0.9475 0.41789
## Year:Season 0 43738 14579.4 3 149.31 3.1137 0.02811
## Sub_region:Season 0 194282 21586.9 9 314.18 4.6104 9.751e-06
##
## Year:Sub_region:Season
## Year:Sub_region
## Year:Season *
## Sub_region:Season ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## DIC_umol_kg ~ Year + Sub_region + Season + (1 | Month) + (1 | SiteID) + Year:Season + Sub_region:Season anova(DIC_in_1) ranova(DIC_in_1) step(DIC_in_1)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 35 -1846.2 3762.4
## (1 | Month) 0 34 -1856.0 3780.0 19.6316 1 9.39e-06 ***
## (1 | SiteID) 0 34 -1848.4 3764.8 4.3996 1 0.03595 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region:Season 1 53301 5922.3 9 302.13 1.2744 0.25011
## Year:Sub_region 2 13312 4437.5 3 311.20 0.9475 0.41789
## Year:Season 0 43738 14579.4 3 149.31 3.1137 0.02811
## Sub_region:Season 0 194282 21586.9 9 314.18 4.6104 9.751e-06
##
## Year:Sub_region:Season
## Year:Sub_region
## Year:Season *
## Sub_region:Season ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## DIC_umol_kg ~ Year + Sub_region + Season + (1 | Month) + (1 | SiteID) + Year:Season + Sub_region:Season DIC_in<-lmer(DIC_umol_kg ~ Year + Sub_region +
(1 | Month) + (1 | SiteID),
data=Trends.inshore)
anova(DIC_in) summary(DIC_in)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DIC_umol_kg ~ Year + Sub_region + (1 | Month) + (1 | SiteID)
## Data: Trends.inshore
##
## REML criterion at convergence: 3971.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1985 -0.6311 -0.0178 0.5971 4.1226
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 7095.6 84.24
## SiteID (Intercept) 251.2 15.85
## Residual 5255.8 72.50
## Number of obs: 347, groups: Month, 12; SiteID, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1505.8479 4868.1758 334.7412 0.309 0.7573
## Year 0.2721 2.4124 334.7290 0.113 0.9103
## Sub_regionUK -16.5526 18.6644 7.1543 -0.887 0.4040
## Sub_regionMK -45.7023 18.5361 6.9628 -2.466 0.0433 *
## Sub_regionLK -70.2130 18.5236 6.9438 -3.790 0.0069 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.027 0.025
## Sub_reginMK -0.014 0.011 0.591
## Sub_reginLK -0.026 0.024 0.590 0.596 plot(DIC_in)
qqnorm(resid(DIC_in))
acf(resid(DIC_in), main="acf(resid(DIC Inshore))")
DIC_in.emmc<-emmeans(DIC_in, ~Sub_region)
DIC_in.emmc<-multcomp::cld(DIC_in.emmc)
#DIC_in.emmc<-as.data.frame(DIC_in.emmc[complete.cases(DIC_in.emmc),])
DIC_in.emmc<-DIC_in.emmc[order(DIC_in.emmc$Sub_region),]
DIC_in.emmc #write.csv(Temp_in.emmc, "FLK_results/Model_9_LTpH_POST.csv")
round(DIC_in.emmc$emmean, digits = 2)## [1] 2054.81 2038.26 2009.11 1984.60 #anova(DIC_in)
#ranova(DIC_in)# Create a new data frame for independent variables
In_Newdata<-expand.grid(Sub_region=unique(Trends.inshore$Sub_region),
Year=seq((min(Trends.inshore$Year)-0),
(max(Trends.inshore$Year)+1)))
In_DIC_Pred <- predict(DIC_in, newdata=In_Newdata,
re.form=NA, level=0, asList = FALSE)
In_DIC_Pred <-cbind(In_Newdata, In_DIC_Pred)
In_DIC_Pred$Date<-as.Date(as.character(In_DIC_Pred$Year), format = "%Y")
# Plot raw data point and fitted model
In_DIC_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(#limits = c(0,6),
name=(expression(~DIC[Ar])),
#limits = c(2.3, 5.2),
#breaks = seq(0, 10, 0.5),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.inshore,
aes(y=DIC_umol_kg, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=21)+
geom_line(data=In_DIC_Pred, aes(y = In_DIC_Pred,
x=Date, colour = Sub_region), linetype=2, size=1)
In_DIC_Modeled
Mid channel
hist(Trends.midchanel$DIC_umol_kg)
shapiro.test(Trends.midchanel$DIC_umol_kg)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.midchanel$DIC_umol_kg
## W = 0.93309, p-value = 8.64e-11DIC_mc_0<-lmer(DIC_umol_kg~Year * Sub_region + (1|Month) +
#(1|Zone)+
(1|SiteID), data=Trends.midchanel)
# DIC_mc_1<-lmer(DIC_umol_kg~Year * Sub_region * Season +
# #(1|Zone)+
# (1|SiteID), data=Trends.midchanel)
#summary(DIC_mc_0)
#summary(DIC_mc_1)
anova(DIC_mc_0)#anova(DIC_mc_1)
#anova(DIC_mc_0, DIC_mc_1)
ranova(DIC_mc_0)step(DIC_mc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1662.8 3347.5
## (1 | SiteID) 1 10 -1663.5 3346.9 1.402 1 0.2363
## (1 | Month) 0 9 -1728.8 3475.7 130.735 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 8960.2 2986.7 3 299.85 1.4641 0.224389
## Year 2 2124.8 2124.8 1 313.93 1.0364 0.309433
## Sub_region 0 29902.5 9967.5 3 304.07 4.8510 0.002598 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## DIC_umol_kg ~ Sub_region + (1 | Month)DIC_mc<-lmer(DIC_umol_kg ~ Year + Sub_region + (1 | Month),
data = Trends.midchanel)
summary(DIC_mc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DIC_umol_kg ~ Year + Sub_region + (1 | Month)
## Data: Trends.midchanel
##
## REML criterion at convergence: 3343.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3732 -0.4413 0.0604 0.5350 3.9154
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 1700 41.23
## Residual 2050 45.28
## Number of obs: 319, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5223.535 3120.132 313.931 1.674 0.095099 .
## Year -1.574 1.546 313.932 -1.018 0.309433
## Sub_regionUK -8.601 8.196 302.877 -1.049 0.294825
## Sub_regionMK -21.738 7.268 302.850 -2.991 0.003011 **
## Sub_regionLK -24.593 7.256 302.976 -3.389 0.000793 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.020 0.018
## Sub_reginMK -0.004 0.003 0.530
## Sub_reginLK -0.026 0.024 0.533 0.602 anova(DIC_mc) plot(DIC_mc)
qqnorm(resid(DIC_mc))
acf(resid(DIC_mc), main="acf(resid(DIC mid Channel))") 
Temp_md.emmc<-emmeans(DIC_mc, ~Sub_region+Year)
Temp_md.emmc<-multcomp::cld(Temp_md.emmc)
#Temp_in.emmc<-as.data.frame(Temp_in.emmc[complete.cases(Temp_in.emmc),])
Temp_md.emmc<-Temp_md.emmc[order(Temp_md.emmc$Sub_region),]
Temp_md.emmc #write.csv(Temp_md.emmc, "FLK_results/Model_10_LTpH_POST.csv")# Create a new data frame for independent variables
Mc_Newdata<-expand.grid(Zone=unique(Trends.midchanel$Zone),
Sub_region=unique(Trends.midchanel$Sub_region),
Year=seq((min(Trends.midchanel$Year)-0), (max(Trends.midchanel$Year)+1)))
Mc_DIC_Pred <- predict(DIC_mc, newdata=Mc_Newdata, re.form=NA, level=0, asList = FALSE)
Mc_DIC_Pred <-cbind(Mc_Newdata, Mc_DIC_Pred)
Mc_DIC_Pred$Date<-as.Date(as.character(Mc_DIC_Pred$Year), format = "%Y")
# Model summary for labels
slope<-round(
summary(DIC_mc)$coefficients["Year", "Estimate"], digits = 3)
ys <- max(Trends.midchanel$DIC_umol_kg, na.rm =T)+ 0.3
# Plot raw data point and fitted model
plot_label <- sprintf("\"\" ~ DIC[Ar] == %0.3f", slope)
plot_label2 <- expression("Year^-1")
Mc_DIC_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(#limits = c(15,40),
#name=(expression("Aragonite saturation state" ~(DIC[Ar]))),
name=(expression(~DIC[Ar])),
limits = c(2.3, 5.2),
breaks = seq(0, 10, 0.5),
expand = c(0.02, 0.02)) +
geom_jitter(data=Trends.midchanel,
aes(y=DIC_umol_kg, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=23)+
geom_line(data=Mc_DIC_Pred, aes(y = Mc_DIC_Pred,
x=Date, colour = Sub_region), linetype=1, size=1)+
# geom_text(data = slope, aes(x = xs, y = ys,
# label = (paste("~DIC[Ar]", "=",
# slope, "Year ^-1"))), size=10)
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
#label = expression("~DIC[Ar] == -0.015** ~Year ^-1"),
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Mc_DIC_Modeled
Offshore
hist(Trends.offshore$DIC_umol_kg)
shapiro.test(Trends.offshore$DIC_umol_kg)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.offshore$DIC_umol_kg
## W = 0.92737, p-value = 8.011e-13DIC_of_0<-lmer(DIC_umol_kg~Year * Sub_region +
(1|Month) + (1|SiteID),
data=Trends.offshore)
#summary(DIC_of_0)
anova(DIC_of_0)ranova(DIC_of_0)step(DIC_of_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1695.8 3413.7
## (1 | SiteID) 1 10 -1695.8 3411.7 0.00 1 1
## (1 | Month) 0 9 -1757.9 3533.8 124.11 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 878.8 292.95 3 370.59 0.8509 0.466791
## Year 2 406.7 406.66 1 384.15 1.1825 0.277525
## Sub_region 0 4526.5 1508.84 3 374.98 4.3971 0.004676 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## DIC_umol_kg ~ Sub_region + (1 | Month)DIC_of<-lmer(DIC_umol_kg ~ Year + Sub_region + (1 | Month),
data =Trends.offshore)
summary(DIC_of)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DIC_umol_kg ~ Year + Sub_region + (1 | Month)
## Data: Trends.offshore
##
## REML criterion at convergence: 3400.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.8843 -0.4329 0.1397 0.6130 2.4102
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 243.8 15.61
## Residual 343.9 18.54
## Number of obs: 390, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 792.3190 1154.5069 384.1392 0.686 0.49295
## Year 0.6221 0.5721 384.1451 1.087 0.27753
## Sub_regionUK -2.9722 2.9870 373.5600 -0.995 0.32037
## Sub_regionMK -9.1133 2.9547 373.5576 -3.084 0.00219 **
## Sub_regionLK -1.3305 2.8028 373.7602 -0.475 0.63528
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.007 0.006
## Sub_reginMK -0.007 0.005 0.590
## Sub_reginLK -0.020 0.019 0.624 0.632 anova(DIC_of) plot(DIC_of)
qqnorm(resid(DIC_of))
acf(resid(DIC_of), main="acf(resid(DIC offshore))") 
Temp_of.emmc<-emmeans(DIC_of, ~Sub_region+Year)
Temp_of.emmc<-multcomp::cld(Temp_of.emmc)
#Temp_in.emmc<-as.data.frame(Temp_in.emmc[complete.cases(Temp_in.emmc),])
Temp_of.emmc<-Temp_of.emmc[order(Temp_of.emmc$Sub_region),]
Temp_of.emmc#write.csv(Temp_of.emmc, "FLK_results/Model_11_LTpH_POST.csv")# Create a new data frame for independent variables
Of_Newdata<-expand.grid(one=unique(Trends.offshore$Zone),
Sub_region=unique(Trends.offshore$Sub_region),
Year=seq((min(Trends.offshore$Year)-0), (max(Trends.offshore$Year)+1)))
Of_DIC_Pred <- predict(DIC_of, newdata=Of_Newdata, re.form=NA, level=0, asList = FALSE)
Of_DIC_Pred <-cbind(Of_Newdata, Of_DIC_Pred)
Of_DIC_Pred$Date<-as.Date(as.character(Of_DIC_Pred$Year), format = "%Y")
# Model summary for labels
slope<-round(summary(DIC_of)$coefficients["Year", "Estimate"], digits = 3)
ys <- max(Trends.offshore$DIC_umol_kg, na.rm =T)
plot_label <- sprintf("\"\" ~ DIC[Ar] == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Of_DIC_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(#limits = c(15,40),
#name=(expression("Aragonite saturation state" ~(DIC[Ar]))),
name=(expression(~DIC[Ar])),
limits = c(3, 4.5),
breaks = seq(0, 10, 0.5),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.offshore,
aes(y=DIC_umol_kg, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=24)+
geom_line(data=Of_DIC_Pred, aes(y = Of_DIC_Pred,
x=Date, colour = Sub_region), linetype=1, size=1)+
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
#label = expression("~DIC[Ar] == -0.015** ~Year ^-1"),
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Of_DIC_Modeled
Oceanic
hist(Trends.ocean$DIC_umol_kg)
shapiro.test(Trends.ocean$DIC_umol_kg)# Normality NOT rejected##
## Shapiro-Wilk normality test
##
## data: Trends.ocean$DIC_umol_kg
## W = 0.92806, p-value = 8.447e-07DIC_oc_0<-lmer(DIC_umol_kg ~ Year * Sub_region +
(1 | Month)+ (1|SiteID),
data=Trends.ocean)
DIC_oc_1<-lmer(DIC_umol_kg ~ Year * Sub_region * Season++ (1|SiteID),
data=Trends.ocean)
#summary(DIC_oc_0)
#summary(DIC_oc_1)
#anova(DIC_oc_1)
anova(DIC_oc_0) ranova(DIC_oc_0) step(DIC_oc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 9 -602.93 1223.8
## (1 | SiteID) 1 8 -602.93 1221.8 0.00 1 1
## (1 | Month) 0 7 -620.68 1255.4 35.51 1 2.537e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 603.06 301.53 2 129.48 1.5503 0.216094
## Sub_region 2 283.86 141.93 2 131.53 0.7231 0.487149
## Year 0 2034.28 2034.28 1 145.68 10.4027 0.001554 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## DIC_umol_kg ~ Year + (1 | Month)DIC_oc<-lmer(DIC_umol_kg ~ Year + (1 | Month), data = Trends.ocean)
summary(DIC_oc)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DIC_umol_kg ~ Year + (1 | Month)
## Data: Trends.ocean
##
## REML criterion at convergence: 1222.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9710 -0.4501 0.0728 0.5284 5.6177
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 181.8 13.48
## Residual 195.6 13.98
## Number of obs: 148, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2368.5433 1369.1200 145.6809 -1.730 0.08575 .
## Year 2.1884 0.6785 145.6797 3.225 0.00155 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Year -1.000 anova(DIC_oc) plot(DIC_oc)
qqnorm(resid(DIC_oc))
acf(resid(DIC_oc), main="acf(resid(DIC open))")
Temp_oc.emmc<-emmeans(DIC_oc, ~Year)
#Temp_oc.emmc<-multcomp::cld(Temp_oc.emmc)
#Temp_oc.emmc<-as.data.frame(Temp_in.emmc[complete.cases(Temp_in.emmc),])
#Temp_oc.emmc<-Temp_oc.emmc[order(Temp_oc.emmc$Sub_region),]
#Temp_oc.emmc# Create a new data frame for independent variables
Oc_Newdata<-expand.grid(one=unique(Trends.ocean$Zone),
Sub_region=unique(Trends.ocean$Sub_region),
Year=seq((min(Trends.ocean$Year)-0), (max(Trends.ocean$Year)+1)))
Oc_DIC_Pred <- predict(DIC_oc, newdata=Oc_Newdata, re.form=NA, level=0, asList = FALSE)
Oc_DIC_Pred <-cbind(Oc_Newdata, Oc_DIC_Pred)
Oc_DIC_Pred$Date<-as.Date(as.character(Oc_DIC_Pred$Year), format = "%Y")
# Model summary for labels
slope<-round(summary(DIC_oc)$coefficients["Year", "Estimate"], digits = 3)
#xs <- as.Date(as.character("2018-11-20"))
#ys <- max(Trends.inshore$DIC_umol_kg, na.rm =T)
ys <- max(Trends.offshore$DIC_umol_kg, na.rm =T)
plot_label <- sprintf("\"\" ~ DIC[Ar] == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Oc_DIC_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(#limits = c(15,40),
#name=(expression("Aragonite saturation state" ~(DIC[Ar]))),
name=(expression(~DIC[Ar])),
limits = c(3, 4.5),
breaks = seq(0, 10, 0.5),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.ocean,
aes(y=DIC_umol_kg, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=25)+
geom_line(data=Oc_DIC_Pred, aes(y = Oc_DIC_Pred,
x=Date), linetype=1, size=1, colour = "black") +
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
#label = expression("~DIC[Ar] == -0.015** ~Year ^-1"),
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Oc_DIC_Modeled
Models summary
Complete models
# ANOVAs
# Convert ANOVA results into dataframes allows for easier name manipulation
av.DIC_in_0 <- data.frame (anova(DIC_in_0))
av.DIC_mc_0 <- data.frame (anova(DIC_mc_0) )
av.DIC_of_0 <- data.frame (anova(DIC_of_0))
av.DIC_oc_0 <- data.frame (anova(DIC_oc_0))
# Putting all into one dataframe/table
anova_DIC_0 <- data.frame(cbind
(c("Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region"),
rbind(av.DIC_in_0, av.DIC_mc_0,
av.DIC_of_0, av.DIC_oc_0)))
colnames(anova_DIC_0) <- c("", "Sum Sq", "Mean Sq", "N Df" , "D Df",
"F value", "Pr(>F)")
row.names(anova_DIC_0) <- NULL
# create HTML table using kableExtra
anova_DIC_0 %>% kable("html", digits=2,
caption="Initial DIC models fixed factors: Year*Region") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "DIC inshore", 1, 3) %>% # groups rows with label
pack_rows(., "DIC mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "DIC offshore", 7, 9) %>% # groups rows with label
pack_rows(., "DIC ocean", 10, 12) | Sum Sq | Mean Sq | N Df | D Df | F value | Pr(>F) | |
|---|---|---|---|---|---|---|
| DIC inshore | ||||||
| Year | 1.33 | 1.33 | 1 | 331.55 | 0.00 | 0.99 |
| Sub_region | 25822.77 | 8607.59 | 3 | 321.52 | 1.65 | 0.18 |
| Year:Sub_region | 25795.61 | 8598.54 | 3 | 321.52 | 1.65 | 0.18 |
| DIC mid-channel | ||||||
| Year | 2653.31 | 2653.31 | 1 | 306.00 | 1.32 | 0.25 |
| Sub_region | 9040.88 | 3013.63 | 3 | 294.59 | 1.50 | 0.21 |
| Year:Sub_region | 9040.52 | 3013.51 | 3 | 294.59 | 1.50 | 0.21 |
| DIC offshore | ||||||
| Year | 435.49 | 435.49 | 1 | 381.47 | 1.26 | 0.26 |
| Sub_region | 882.69 | 294.23 | 3 | 370.58 | 0.85 | 0.46 |
| Year:Sub_region | 878.84 | 292.95 | 3 | 370.58 | 0.85 | 0.47 |
| DIC ocean | ||||||
| Year | 1647.23 | 1647.23 | 1 | 141.16 | 8.47 | 0.00 |
| Sub_region | 602.62 | 301.31 | 2 | 129.49 | 1.55 | 0.22 |
| Year:Sub_region | 603.06 | 301.53 | 2 | 129.49 | 1.55 | 0.22 |
# R-ANOVAs
rav.DIC_in_0 <- data.frame (ranova(DIC_in_0))
rav.DIC_mc_0 <- data.frame (ranova(DIC_mc_0) )
rav.DIC_of_0 <- data.frame (ranova(DIC_of_0))
rav.DIC_oc_0 <- data.frame (ranova(DIC_oc_0))
# Putting all into one dataframe/table
ranova_DIC_0 <- data.frame(cbind
(c("<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)"),
rbind(rav.DIC_in_0, rav.DIC_mc_0,
rav.DIC_of_0, rav.DIC_oc_0)))
colnames(ranova_DIC_0) <- c("", "npar", "logLik", "AIC" , "LRT",
"DF", "Pr(>Chi Sq)")
row.names(ranova_DIC_0) <- NULL
# create HTML table using kableExtra
ranova_DIC_0 %>% kable("html", digits=2,
caption="Initial DIC models random factors: Month+Station") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "DIC inshore", 1, 3) %>% # groups rows with label
pack_rows(., "DIC mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "DIC offshore", 7, 9) %>% # groups rows with label
pack_rows(., "DIC ocean", 10, 12) | npar | logLik | AIC | LRT | DF | Pr(>Chi Sq) | |
|---|---|---|---|---|---|---|
| DIC inshore | ||||||
| <none> | 11 | -1975.88 | 3973.76 | |||
| (1 | Month) | 10 | -2094.12 | 4208.24 | 236.47 | 1 | 0.00 |
| (1 | SiteID) | 10 | -1977.99 | 3975.98 | 4.22 | 1 | 0.04 |
| DIC mid-channel | ||||||
| <none> | 11 | -1662.77 | 3347.54 | |||
| (1 | Month) | 10 | -1728.78 | 3477.56 | 132.03 | 1 | 0.00 |
| (1 | SiteID) | 10 | -1663.47 | 3346.94 | 1.40 | 1 | 0.24 |
| DIC offshore | ||||||
| <none> | 11 | -1695.84 | 3413.68 | |||
| (1 | Month) | 10 | -1757.89 | 3535.78 | 124.10 | 1 | 0.00 |
| (1 | SiteID) | 10 | -1695.84 | 3411.68 | 0.00 | 1 | 1.00 |
| DIC ocean | ||||||
| <none> | 9 | -602.93 | 1223.85 | |||
| (1 | Month) | 8 | -620.68 | 1257.36 | 35.51 | 1 | 0.00 |
| (1 | SiteID) | 8 | -602.93 | 1221.85 | 0.00 | 1 | 1.00 |
DIC_models_0<-list("Inshore" = DIC_in_0,
"Mid channel" = DIC_mc_0,
"Offshore" = DIC_of_0,
"Ocean" = DIC_oc_0)
modelsummary(DIC_models_0, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'Initial DIC model outputs (all factors)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | −1.489 | −1.711 | 0.411 | 2.017 |
| (4.456) | (2.801) | (1.149) | (1.333) | |
| Intercept | 5059.086 | 5499.903 | 1218.372 | −2025.942 |
| (8991.623) | (5652.616) | (2318.160) | (2689.262) | |
| Upper Keys | 2656.414 | 1959.568 | −317.924 | |
| (11390.728) | (7765.744) | (2873.140) | ||
| Middle Keys | −16751.972 | −7160.010 | −2728.834 | −2291.926 |
| (11039.360) | (6870.578) | (2846.080) | (3052.621) | |
| Lower Keys | 2257.089 | 5776.461 | 1062.283 | 1975.620 |
| (11122.331) | (6937.582) | (2711.348) | (3097.148) | |
| Num.Obs. | 347 | 319 | 390 | 148 |
| R2 Marg. | 0.058 | 0.035 | 0.043 | 0.122 |
| R2 Cond. | 0.609 | 0.487 | ||
| AIC | 3973.8 | 3347.5 | 3413.7 | 1223.9 |
| BIC | 4016.1 | 3389.0 | 3457.3 | 1250.8 |
| ICC | 0.6 | 0.5 | ||
| RMSE | 69.85 | 43.28 | 18.11 | 13.17 |
| + p |
#modelsummary(DIC_models, estimate = "p.value")Models with non-significant factors removed
DIC_models<-list("Inshore" = DIC_in,
"Mid channel" = DIC_mc,
"Offshore" = DIC_of,
"Ocean" = DIC_oc)
modelsummary(DIC_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'Final DIC model outputs (dropped factors)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | 0.272 | −1.574 | 0.622 | 2.188** |
| (2.412) | (1.546) | (0.572) | (0.679) | |
| Intercept | 1505.848 | 5223.535+ | 792.319 | −2368.543+ |
| (4868.176) | (3120.132) | (1154.507) | (1369.120) | |
| Upper Keys | −16.553 | −8.601 | −2.972 | |
| (18.664) | (8.196) | (2.987) | ||
| Middle Keys | −45.702* | −21.738** | −9.113** | |
| (18.536) | (7.268) | (2.955) | ||
| Lower Keys | −70.213*** | −24.593*** | −1.330 | |
| (18.524) | (7.256) | (2.803) | ||
| Num.Obs. | 347 | 319 | 390 | 148 |
| R2 Marg. | 0.053 | 0.028 | 0.022 | 0.054 |
| R2 Cond. | 0.605 | 0.468 | 0.428 | 0.510 |
| AIC | 3987.6 | 3357.6 | 3414.6 | 1230.9 |
| BIC | 4018.3 | 3384.0 | 3442.4 | 1242.9 |
| ICC | 0.6 | 0.5 | 0.4 | 0.5 |
| RMSE | 70.39 | 44.17 | 18.17 | 13.41 |
| + p |
modelsummary(DIC_models, output = "FLK_results/Figures/Fig_2/Trend_DIC.docx")
#modelsummary(DIC_models, estimate = "p.value")Trends_DIC<-ggarrange(In_DIC_Modeled+ theme(
#axis.text.x=element_blank(),
legend.position = "none"),
Mc_DIC_Modeled+ theme(legend.position = "none",
#axis.text.x=element_blank(),
axis.title.y=element_blank()),
Of_DIC_Modeled + theme(legend.position = "none",
#axis.text.x=element_blank(),
axis.title.y=element_blank()),
Oc_DIC_Modeled + theme(legend.position = "none",
#axis.text.x=element_blank(),
axis.title.y=element_blank()),
labels = c("C"),
ncol = 4, nrow = 1, widths =c(1.1, 1, 1, 1))
Trends_DIC
Omega-All
##
## Shapiro-Wilk normality test
##
## data: Trends.data$Aragonite_Sat_W
## W = 0.96676, p-value = 5.771e-16
## Model with all data
Omega_all_o<-lmer(Aragonite_Sat_W~Year * Zone * Sub_region + (1|Month)+
(1|SiteID), data=Trends.data)
step(Omega_all_o)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 33 -302.60 671.20
## (1 | SiteID) 1 32 -303.37 670.74 1.536 1 0.2153
## (1 | Month) 0 31 -453.42 968.83 300.097 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Zone:Sub_region 1 0.1701 0.02126 8 1161.0 0.2546 0.979753
## Year:Sub_region 2 0.1190 0.03967 3 1169.2 0.4775 0.698004
## Year:Zone 0 1.0532 0.35107 3 1172.0 4.2313 0.005506
## Zone:Sub_region 0 6.5766 0.82208 8 1172.0 9.9081 2.057e-13
##
## Year:Zone:Sub_region
## Year:Sub_region
## Year:Zone **
## Zone:Sub_region ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## Aragonite_Sat_W ~ Year + Zone + Sub_region + (1 | Month) + Year:Zone + Zone:Sub_regionOmega_all_1<-lmer(Aragonite_Sat_W ~ Year + Zone + Sub_region + (1 | Month) +
Year:Zone + Zone:Sub_region, data=Trends.data)
Omega_all_3<-lmer(Aragonite_Sat_W ~ Year + Zone + (1 | Month) + (1|Sub_region)+
Year:Zone, data=Trends.data)
anova(Omega_all_1, Omega_all_3) summary(Omega_all_1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Aragonite_Sat_W ~ Year + Zone + Sub_region + (1 | Month) + Year:Zone +
## Zone:Sub_region
## Data: Trends.data
##
## REML criterion at convergence: 544.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.6636 -0.5794 0.0809 0.5676 5.1487
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.03006 0.1734
## Residual 0.08297 0.2880
## Number of obs: 1202, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -3.176e+01 1.636e+01 1.183e+03 -1.942 0.05239
## Year 1.755e-02 8.105e-03 1.183e+03 2.165 0.03057
## ZoneMid channel 2.090e+01 2.164e+01 1.172e+03 0.966 0.33414
## ZoneOffshore 5.479e+01 2.060e+01 1.172e+03 2.659 0.00793
## ZoneOceanic 8.328e+01 2.707e+01 1.172e+03 3.077 0.00214
## Sub_regionUK 2.163e-01 4.677e-02 1.172e+03 4.625 4.16e-06
## Sub_regionMK 9.860e-02 4.598e-02 1.172e+03 2.144 0.03220
## Sub_regionLK -3.035e-01 4.582e-02 1.172e+03 -6.624 5.33e-11
## Year:ZoneMid channel -1.032e-02 1.072e-02 1.172e+03 -0.962 0.33603
## Year:ZoneOffshore -2.709e-02 1.021e-02 1.172e+03 -2.654 0.00807
## Year:ZoneOceanic -4.106e-02 1.341e-02 1.172e+03 -3.061 0.00226
## ZoneMid channel:Sub_regionUK -6.944e-02 7.000e-02 1.172e+03 -0.992 0.32145
## ZoneOffshore:Sub_regionUK -1.756e-01 6.587e-02 1.172e+03 -2.666 0.00778
## ZoneOceanic:Sub_regionUK -4.433e-01 7.992e-02 1.172e+03 -5.546 3.60e-08
## ZoneMid channel:Sub_regionMK -3.939e-02 6.515e-02 1.172e+03 -0.605 0.54554
## ZoneOffshore:Sub_regionMK -7.577e-02 6.494e-02 1.172e+03 -1.167 0.24350
## ZoneOceanic:Sub_regionMK -3.815e-01 6.672e-02 1.172e+03 -5.718 1.37e-08
## ZoneMid channel:Sub_regionLK 2.028e-01 6.488e-02 1.172e+03 3.126 0.00181
## ZoneOffshore:Sub_regionLK 2.626e-01 6.313e-02 1.172e+03 4.160 3.42e-05
##
## (Intercept) .
## Year *
## ZoneMid channel
## ZoneOffshore **
## ZoneOceanic **
## Sub_regionUK ***
## Sub_regionMK *
## Sub_regionLK ***
## Year:ZoneMid channel
## Year:ZoneOffshore **
## Year:ZoneOceanic **
## ZoneMid channel:Sub_regionUK
## ZoneOffshore:Sub_regionUK **
## ZoneOceanic:Sub_regionUK ***
## ZoneMid channel:Sub_regionMK
## ZoneOffshore:Sub_regionMK
## ZoneOceanic:Sub_regionMK ***
## ZoneMid channel:Sub_regionLK **
## ZoneOffshore:Sub_regionLK ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficient anova(Omega_all_1) ranova(Omega_all_1)# Obtain slopes
#ranef(Omega_all_1)
#fixef(Omega_all_1)
#coef(Omega_all_1)$Month
#coef(Omega_all_1)$Zone
m.lst <- lstrends(Omega_all_1, "Zone", var="Year")
m.lst## Zone Year.trend SE df lower.CL upper.CL
## Inshore 0.01755 0.00812 1183 0.00162 0.033479
## Mid channel 0.00723 0.00837 1183 -0.00919 0.023650
## Offshore -0.00954 0.00768 1183 -0.02462 0.005531
## Oceanic -0.02351 0.01160 1180 -0.04627 -0.000745
##
## Results are averaged over the levels of: Sub_region
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95 pairs(m.lst)## contrast estimate SE df t.ratio p.value
## Inshore - Mid channel 0.0103 0.0107 1172 0.962 0.7708
## Inshore - Offshore 0.0271 0.0102 1172 2.654 0.0402
## Inshore - Oceanic 0.0411 0.0134 1172 3.061 0.0121
## Mid channel - Offshore 0.0168 0.0104 1172 1.611 0.3726
## Mid channel - Oceanic 0.0307 0.0136 1172 2.266 0.1067
## Offshore - Oceanic 0.0140 0.0132 1172 1.061 0.7136
##
## Results are averaged over the levels of: Sub_region
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 4 estimatesInshore
# Inshore Omega
hist(Trends.inshore$Aragonite_Sat_W)
shapiro.test(Trends.inshore$Aragonite_Sat_W) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.inshore$Aragonite_Sat_W
## W = 0.99052, p-value = 0.02452 Omega_in_0<-lmer(Aragonite_Sat_W~Year * Sub_region + (1|Month)+
#(1|Zone)+
(1|SiteID), data=Trends.inshore)
Omega_in_1<-lmer(Aragonite_Sat_W~Year * Sub_region * Season +
#(1|Zone)+
(1|SiteID), data=Trends.inshore)
#summary(Omega_in_0)
#summary(Omega_in_1)
#anova(Omega_in_0, Omega_in_1)
#step(Omega_in_1)
anova(Omega_in_0) ranova(Omega_in_0) step(Omega_in_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -158.84 339.69
## (1 | Month) 0 10 -270.61 561.22 223.534 1 < 2e-16 ***
## (1 | SiteID) 0 10 -160.73 341.46 3.778 1 0.05193 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 0.0355 0.01184 3 321.51 0.1021 0.958800
## Year 2 0.0130 0.01299 1 334.99 0.1129 0.737082
## Sub_region 0 5.1444 1.71480 3 6.96 14.9425 0.002036 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## Aragonite_Sat_W ~ Sub_region + (1 | Month) + (1 | SiteID) Omega_in<-lmer(Aragonite_Sat_W ~ Sub_region + (1 | Month),
data=Trends.inshore)
anova(Omega_in) summary(Omega_in)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Aragonite_Sat_W ~ Sub_region + (1 | Month)
## Data: Trends.inshore
##
## REML criterion at convergence: 297.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5158 -0.5670 0.0272 0.5863 3.9500
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.1355 0.3681
## Residual 0.1184 0.3441
## Number of obs: 347, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.65604 0.11450 13.98288 31.930 1.82e-14 ***
## Sub_regionUK 0.20119 0.05589 332.16472 3.600 0.000367 ***
## Sub_regionMK 0.09851 0.05498 332.15825 1.792 0.074077 .
## Sub_regionLK -0.29697 0.05485 332.21792 -5.414 1.18e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sb_rUK Sb_rMK
## Sub_reginUK -0.277
## Sub_reginMK -0.283 0.576
## Sub_reginLK -0.284 0.575 0.589 plot(Omega_in)
qqnorm(resid(Omega_in))
acf(resid(Omega_in), main="acf(resid(Omega Inshore))")
Arg_in.emmc<-emmeans(Omega_in, ~Sub_region)
Arg_in.emmc<-multcomp::cld(Arg_in.emmc)
#Arg_in.emmc<-as.data.frame(Arg_in.emmc[complete.cases(Arg_in.emmc),])
Arg_in.emmc<-Arg_in.emmc[order(Arg_in.emmc$Sub_region),]
Arg_in.emmc #write.csv(Arg_in.emmc, "FLK_results/Model_9_LTpH_POST.csv")
round(Arg_in.emmc$emmean, digits = 2)## [1] 3.66 3.86 3.75 3.36 #anova(Omega_in)
#ranova(Omega_in)# Create a new data frame for independent variables
In_Newdata<-expand.grid(Zone=unique(Trends.inshore$Zone),
Sub_region=unique(Trends.inshore$Sub_region),
Year=seq((min(Trends.inshore$Year)-0), (max(Trends.inshore$Year)+1)))
In_Omega_Pred <- predict(Omega_in, newdata=In_Newdata, re.form=NA, level=0, asList = FALSE)
In_Omega_Pred <-cbind(In_Newdata, In_Omega_Pred)
In_Omega_Pred$Date<-as.Date(as.character(In_Omega_Pred$Year), format = "%Y")
# Plot raw data point and fitted model
In_Omega_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(#limits = c(0,6),
#name=(expression("Aragonite saturation state" ~(Omega[Ar]))),
name=(expression(~Omega[Ar])),
limits = c(2.3, 5.2),
breaks = seq(0, 10, 0.5),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.inshore,
aes(y=Aragonite_Sat_W, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=21)+
geom_line(data=In_Omega_Pred, aes(y = In_Omega_Pred,
x=Date, colour = Sub_region), linetype=2, size=1)
In_Omega_Modeled
Mid channel
hist(Trends.midchanel$Aragonite_Sat_W)
shapiro.test(Trends.midchanel$Aragonite_Sat_W)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.midchanel$Aragonite_Sat_W
## W = 0.97677, p-value = 4.94e-05Omega_mc_0<-lmer(Aragonite_Sat_W~Year * Sub_region + (1|Month) +
#(1|Zone)+
(1|SiteID), data=Trends.midchanel)
# Omega_mc_1<-lmer(Aragonite_Sat_W~Year * Sub_region * Season +
# #(1|Zone)+
# (1|SiteID), data=Trends.midchanel)
#summary(Omega_mc_0)
#summary(Omega_mc_1)
anova(Omega_mc_0)#anova(Omega_mc_1)
#anova(Omega_mc_0, Omega_mc_1)
ranova(Omega_mc_0)step(Omega_mc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -17.523 57.046
## (1 | SiteID) 1 10 -18.446 56.891 1.845 1 0.1744
## (1 | Month) 0 9 -82.815 183.630 128.739 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 0.05093 0.01698 3 300.21 0.3253 0.8071
## Year 2 0.02537 0.02537 1 313.50 0.4895 0.4847
## Sub_region 0 2.54574 0.84858 3 304.19 16.3875 6.746e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## Aragonite_Sat_W ~ Sub_region + (1 | Month)Omega_mc<-lmer(Aragonite_Sat_W ~ Year + Sub_region + (1 | Month),
data = Trends.midchanel)
summary(Omega_mc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Aragonite_Sat_W ~ Year + Sub_region + (1 | Month)
## Data: Trends.midchanel
##
## REML criterion at convergence: 18.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4323 -0.5794 0.0884 0.6009 3.5273
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.03750 0.1936
## Residual 0.05184 0.2277
## Number of obs: 319, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -7.215226 15.649192 313.494300 -0.461 0.645075
## Year 0.005426 0.007755 313.497742 0.700 0.484664
## Sub_regionUK 0.149803 0.041212 303.192891 3.635 0.000326 ***
## Sub_regionMK 0.058704 0.036548 303.162849 1.606 0.109271
## Sub_regionLK -0.099670 0.036486 303.301923 -2.732 0.006669 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.020 0.018
## Sub_reginMK -0.004 0.003 0.530
## Sub_reginLK -0.025 0.024 0.533 0.602 anova(Omega_mc) plot(Omega_mc)
qqnorm(resid(Omega_mc))
acf(resid(Omega_mc), main="acf(resid(Omega mid Channel))") 
Arg_md.emmc<-emmeans(Omega_mc, ~Sub_region+Year)
Arg_md.emmc<-multcomp::cld(Arg_md.emmc)
#Arg_in.emmc<-as.data.frame(Arg_in.emmc[complete.cases(Arg_in.emmc),])
Arg_md.emmc<-Arg_md.emmc[order(Arg_md.emmc$Sub_region),]
Arg_md.emmc #write.csv(Arg_md.emmc, "FLK_results/Model_10_LTpH_POST.csv")# Create a new data frame for independent variables
Mc_Newdata<-expand.grid(Zone=unique(Trends.midchanel$Zone),
Sub_region=unique(Trends.midchanel$Sub_region),
Year=seq((min(Trends.midchanel$Year)-0), (max(Trends.midchanel$Year)+1)))
Mc_Omega_Pred <- predict(Omega_mc, newdata=Mc_Newdata, re.form=NA, level=0, asList = FALSE)
Mc_Omega_Pred <-cbind(Mc_Newdata, Mc_Omega_Pred)
Mc_Omega_Pred$Date<-as.Date(as.character(Mc_Omega_Pred$Year), format = "%Y")
# Model summary for labels
slope<-round(
summary(Omega_mc)$coefficients["Year", "Estimate"], digits = 3)
ys <- max(Trends.midchanel$Aragonite_Sat_W, na.rm =T)+ 0.3
# Plot raw data point and fitted model
plot_label <- sprintf("\"\" ~ Omega[Ar] == %0.3f", slope)
plot_label2 <- expression("Year^-1")
Mc_Omega_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(#limits = c(15,40),
#name=(expression("Aragonite saturation state" ~(Omega[Ar]))),
name=(expression(~Omega[Ar])),
limits = c(2.3, 5.2),
breaks = seq(0, 10, 0.5),
expand = c(0.02, 0.02)) +
geom_jitter(data=Trends.midchanel,
aes(y=Aragonite_Sat_W, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=23)+
geom_line(data=Mc_Omega_Pred, aes(y = Mc_Omega_Pred,
x=Date, colour = Sub_region), linetype=1, size=1)+
# geom_text(data = slope, aes(x = xs, y = ys,
# label = (paste("~Omega[Ar]", "=",
# slope, "Year ^-1"))), size=10)
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
#label = expression("~Omega[Ar] == -0.015** ~Year ^-1"),
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Mc_Omega_Modeled
Offshore
hist(Trends.offshore$Aragonite_Sat_W)
shapiro.test(Trends.offshore$Aragonite_Sat_W)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.offshore$Aragonite_Sat_W
## W = 0.99379, p-value = 0.1136Omega_of_0<-lmer(Aragonite_Sat_W~Year * Sub_region +
(1|Month) + (1|SiteID),
data=Trends.offshore)
#summary(Omega_of_0)
anova(Omega_of_0)ranova(Omega_of_0)step(Omega_of_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 208.49 -394.97
## (1 | SiteID) 1 10 207.77 -395.53 1.438 1 0.2305
## (1 | Month) 0 9 112.36 -206.72 190.817 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 0.06605 0.022018 3 368.71 1.3883 0.2460
## Year 2 0.02010 0.020105 1 382.74 1.2636 0.2617
## Sub_region 0 0.46970 0.156565 3 372.88 9.8508 2.883e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## Aragonite_Sat_W ~ Sub_region + (1 | Month)Omega_of<-lmer(Aragonite_Sat_W ~ Year + Sub_region + (1 | Month),
data =Trends.offshore)
summary(Omega_of)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Aragonite_Sat_W ~ Year + Sub_region + (1 | Month)
## Data: Trends.offshore
##
## REML criterion at convergence: -434.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9928 -0.5880 0.0290 0.6318 3.7875
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.01749 0.1322
## Residual 0.01591 0.1261
## Number of obs: 388, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 12.654489 7.902638 382.739787 1.601 0.11014
## Year -0.004402 0.003916 382.735971 -1.124 0.26167
## Sub_regionUK 0.030178 0.020318 371.700710 1.485 0.13831
## Sub_regionMK 0.023864 0.020098 371.699196 1.187 0.23584
## Sub_regionLK -0.051178 0.019121 371.848012 -2.677 0.00777 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.008 0.006
## Sub_reginMK -0.007 0.006 0.590
## Sub_reginLK -0.021 0.019 0.622 0.630 anova(Omega_of) plot(Omega_of)
qqnorm(resid(Omega_of))
acf(resid(Omega_of), main="acf(resid(Omega offshore))") 
Arg_of.emmc<-emmeans(Omega_of, ~Sub_region+Year)
Arg_of.emmc<-multcomp::cld(Arg_of.emmc)
#Arg_in.emmc<-as.data.frame(Arg_in.emmc[complete.cases(Arg_in.emmc),])
Arg_of.emmc<-Arg_of.emmc[order(Arg_of.emmc$Sub_region),]
Arg_of.emmc#write.csv(Arg_of.emmc, "FLK_results/Model_11_LTpH_POST.csv")# Create a new data frame for independent variables
Of_Newdata<-expand.grid(one=unique(Trends.offshore$Zone),
Sub_region=unique(Trends.offshore$Sub_region),
Year=seq((min(Trends.offshore$Year)-0), (max(Trends.offshore$Year)+1)))
Of_Omega_Pred <- predict(Omega_of, newdata=Of_Newdata, re.form=NA, level=0, asList = FALSE)
Of_Omega_Pred <-cbind(Of_Newdata, Of_Omega_Pred)
Of_Omega_Pred$Date<-as.Date(as.character(Of_Omega_Pred$Year), format = "%Y")
# Model summary for labels
slope<-round(summary(Omega_of)$coefficients["Year", "Estimate"], digits = 3)
ys <- max(Trends.offshore$Aragonite_Sat_W, na.rm =T)
plot_label <- sprintf("\"\" ~ Omega[Ar] == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Of_Omega_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(#limits = c(15,40),
#name=(expression("Aragonite saturation state" ~(Omega[Ar]))),
name=(expression(~Omega[Ar])),
limits = c(3, 4.5),
breaks = seq(0, 10, 0.5),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.offshore,
aes(y=Aragonite_Sat_W, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=24)+
geom_line(data=Of_Omega_Pred, aes(y = Of_Omega_Pred,
x=Date, colour = Sub_region), linetype=1, size=1)+
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
#label = expression("~Omega[Ar] == -0.015** ~Year ^-1"),
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Of_Omega_Modeled
Oceanic
hist(Trends.ocean$Aragonite_Sat_W)
shapiro.test(Trends.ocean$Aragonite_Sat_W)# Normality NOT rejected##
## Shapiro-Wilk normality test
##
## data: Trends.ocean$Aragonite_Sat_W
## W = 0.90529, p-value = 3.144e-08Omega_oc_0<-lmer(Aragonite_Sat_W ~ Year * Sub_region +
(1 | Month)+ (1|SiteID),
data=Trends.ocean)
Omega_oc_1<-lmer(Aragonite_Sat_W ~ Year * Sub_region * Season++ (1|SiteID),
data=Trends.ocean)
#summary(Omega_oc_0)
#summary(Omega_oc_1)
#anova(Omega_oc_1)
anova(Omega_oc_0) ranova(Omega_oc_0) step(Omega_oc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 9 63.501 -109.001
## (1 | SiteID) 1 8 63.501 -111.001 0.00 1 1
## (1 | Month) 0 7 12.375 -10.751 102.25 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 0.039379 0.019689 2 130.49 1.3101 0.2733
## Year 2 0.016473 0.016473 1 138.27 1.0905 0.2982
## Sub_region 3 0.056208 0.028104 2 133.73 1.8700 0.1581
##
## Model found:
## Aragonite_Sat_W ~ (1 | Month)Omega_oc<-lmer(Aragonite_Sat_W ~ Year + (1 | Month), data = Trends.ocean)
summary(Omega_oc)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Aragonite_Sat_W ~ Year + (1 | Month)
## Data: Trends.ocean
##
## REML criterion at convergence: -146.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.4685 -0.5327 0.0287 0.5929 2.3286
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.04342 0.2084
## Residual 0.01530 0.1237
## Number of obs: 148, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 17.075542 12.373891 140.415184 1.380 0.170
## Year -0.006584 0.006132 140.409085 -1.074 0.285
##
## Correlation of Fixed Effects:
## (Intr)
## Year -1.000 anova(Omega_oc) plot(Omega_oc)
qqnorm(resid(Omega_oc))
acf(resid(Omega_oc), main="acf(resid(Omega open))")
Arg_oc.emmc<-emmeans(Omega_oc, ~Year)
#Arg_oc.emmc<-multcomp::cld(Arg_oc.emmc)
#Arg_oc.emmc<-as.data.frame(Arg_in.emmc[complete.cases(Arg_in.emmc),])
#Arg_oc.emmc<-Arg_oc.emmc[order(Arg_oc.emmc$Sub_region),]
#Arg_oc.emmc# Create a new data frame for independent variables
Oc_Newdata<-expand.grid(one=unique(Trends.ocean$Zone),
Sub_region=unique(Trends.ocean$Sub_region),
Year=seq((min(Trends.ocean$Year)-0), (max(Trends.ocean$Year)+1)))
Oc_Omega_Pred <- predict(Omega_oc, newdata=Oc_Newdata, re.form=NA, level=0, asList = FALSE)
Oc_Omega_Pred <-cbind(Oc_Newdata, Oc_Omega_Pred)
Oc_Omega_Pred$Date<-as.Date(as.character(Oc_Omega_Pred$Year), format = "%Y")
# Model summary for labels
slope<-round(summary(Omega_oc)$coefficients["Year", "Estimate"], digits = 3)
#xs <- as.Date(as.character("2018-11-20"))
#ys <- max(Trends.inshore$Aragonite_Sat_W, na.rm =T)
ys <- max(Trends.offshore$Aragonite_Sat_W, na.rm =T)
plot_label <- sprintf("\"\" ~ Omega[Ar] == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Oc_Omega_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(#limits = c(15,40),
#name=(expression("Aragonite saturation state" ~(Omega[Ar]))),
name=(expression(~Omega[Ar])),
limits = c(3, 4.5),
breaks = seq(0, 10, 0.5),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.ocean,
aes(y=Aragonite_Sat_W, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=25)+
geom_line(data=Oc_Omega_Pred, aes(y = Oc_Omega_Pred,
x=Date), linetype=1, size=1, colour = "black") +
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
#label = expression("~Omega[Ar] == -0.015** ~Year ^-1"),
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Oc_Omega_Modeled
Models summary
Complete models
# ANOVAs
# Convert ANOVA results into dataframes allows for easier name manipulation
av.Omega_in_0 <- data.frame (anova(Omega_in_0))
av.Omega_mc_0 <- data.frame (anova(Omega_mc_0) )
av.Omega_of_0 <- data.frame (anova(Omega_of_0))
av.Omega_oc_0 <- data.frame (anova(Omega_oc_0))
# Putting all into one dataframe/table
anova_Omega_0 <- data.frame(cbind
(c("Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region"),
rbind(av.Omega_in_0, av.Omega_mc_0,
av.Omega_of_0, av.Omega_oc_0)))
colnames(anova_Omega_0) <- c("", "Sum Sq", "Mean Sq", "N Df" , "D Df",
"F value", "Pr(>F)")
row.names(anova_Omega_0) <- NULL
# create HTML table using kableExtra
anova_Omega_0 %>% kable("html", digits=2,
caption="Initial omega models fixed factors: Year*Region") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Omega inshore", 1, 3) %>% # groups rows with label
pack_rows(., "Omega mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "Omega offshore", 7, 9) %>% # groups rows with label
pack_rows(., "Omega ocean", 10, 12) | Sum Sq | Mean Sq | N Df | D Df | F value | Pr(>F) | |
|---|---|---|---|---|---|---|
| Omega inshore | ||||||
| Year | 0.01 | 0.01 | 1 | 332.03 | 0.08 | 0.78 |
| Sub_region | 0.04 | 0.01 | 3 | 321.51 | 0.10 | 0.96 |
| Year:Sub_region | 0.04 | 0.01 | 3 | 321.51 | 0.10 | 0.96 |
| Omega mid-channel | ||||||
| Year | 0.01 | 0.01 | 1 | 305.60 | 0.28 | 0.60 |
| Sub_region | 0.05 | 0.02 | 3 | 294.65 | 0.34 | 0.79 |
| Year:Sub_region | 0.05 | 0.02 | 3 | 294.65 | 0.34 | 0.80 |
| Omega offshore | ||||||
| Year | 0.03 | 0.03 | 1 | 371.83 | 1.77 | 0.18 |
| Sub_region | 0.07 | 0.02 | 3 | 360.94 | 1.41 | 0.24 |
| Year:Sub_region | 0.07 | 0.02 | 3 | 360.94 | 1.40 | 0.24 |
| Omega ocean | ||||||
| Year | 0.01 | 0.01 | 1 | 135.81 | 0.65 | 0.42 |
| Sub_region | 0.04 | 0.02 | 2 | 130.49 | 1.31 | 0.27 |
| Year:Sub_region | 0.04 | 0.02 | 2 | 130.49 | 1.31 | 0.27 |
# R-ANOVAs
rav.Omega_in_0 <- data.frame (ranova(Omega_in_0))
rav.Omega_mc_0 <- data.frame (ranova(Omega_mc_0) )
rav.Omega_of_0 <- data.frame (ranova(Omega_of_0))
rav.Omega_oc_0 <- data.frame (ranova(Omega_oc_0))
# Putting all into one dataframe/table
ranova_Omega_0 <- data.frame(cbind
(c("<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)"),
rbind(rav.Omega_in_0, rav.Omega_mc_0,
rav.Omega_of_0, rav.Omega_oc_0)))
colnames(ranova_Omega_0) <- c("", "npar", "logLik", "AIC" , "LRT",
"DF", "Pr(>Chi Sq)")
row.names(ranova_Omega_0) <- NULL
# create HTML table using kableExtra
ranova_Omega_0 %>% kable("html", digits=2,
caption="Initial omega models random factors: Month+Station") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Omega inshore", 1, 3) %>% # groups rows with label
pack_rows(., "Omega mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "Omega offshore", 7, 9) %>% # groups rows with label
pack_rows(., "Omega ocean", 10, 12) | npar | logLik | AIC | LRT | DF | Pr(>Chi Sq) | |
|---|---|---|---|---|---|---|
| Omega inshore | ||||||
| <none> | 11 | -158.84 | 339.69 | |||
| (1 | Month) | 10 | -270.61 | 561.22 | 223.53 | 1 | 0.00 |
| (1 | SiteID) | 10 | -160.73 | 341.46 | 3.78 | 1 | 0.05 |
| Omega mid-channel | ||||||
| <none> | 11 | -17.52 | 57.05 | |||
| (1 | Month) | 10 | -82.68 | 185.37 | 130.32 | 1 | 0.00 |
| (1 | SiteID) | 10 | -18.45 | 56.89 | 1.84 | 1 | 0.17 |
| Omega offshore | ||||||
| <none> | 11 | 208.48 | -394.97 | |||
| (1 | Month) | 10 | 112.36 | -204.72 | 192.25 | 1 | 0.00 |
| (1 | SiteID) | 10 | 207.77 | -395.53 | 1.44 | 1 | 0.23 |
| Omega ocean | ||||||
| <none> | 9 | 63.50 | -109.00 | |||
| (1 | Month) | 8 | 12.38 | -8.75 | 102.25 | 1 | 0.00 |
| (1 | SiteID) | 8 | 63.50 | -111.00 | 0.00 | 1 | 1.00 |
Omega_models_0<-list("Inshore" = Omega_in_0,
"Mid channel" = Omega_mc_0,
"Offshore" = Omega_of_0,
"Ocean" = Omega_oc_0)
modelsummary(Omega_models_0, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'Initial Omega model outputs (all factors)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | −0.006 | −0.001 | −0.008 | −0.002 |
| (0.021) | (0.014) | (0.008) | (0.012) | |
| Intercept | 15.023 | 6.371 | 20.923 | 8.543 |
| (42.358) | (28.519) | (15.637) | (23.794) | |
| Upper Keys | −20.765 | 2.644 | −12.566 | |
| (53.687) | (39.216) | (19.350) | ||
| Middle Keys | −27.618 | −18.546 | 8.870 | 25.277 |
| (52.031) | (34.693) | (19.165) | (26.849) | |
| Lower Keys | −22.317 | −27.783 | −22.561 | −8.359 |
| (52.422) | (35.031) | (18.267) | (27.232) | |
| Num.Obs. | 347 | 319 | 388 | 148 |
| R2 Marg. | 0.127 | 0.084 | 0.042 | 0.053 |
| R2 Cond. | 0.606 | 0.480 | 0.554 | |
| AIC | 339.7 | 57.0 | −395.0 | −109.0 |
| BIC | 382.0 | 98.5 | −351.4 | −82.0 |
| ICC | 0.5 | 0.4 | 0.5 | |
| RMSE | 0.33 | 0.22 | 0.12 | 0.12 |
| + p |
#modelsummary(Omega_models, estimate = "p.value")Models with non-significant factors removed
Omega_models<-list("Inshore" = Omega_in,
"Mid channel" = Omega_mc,
"Offshore" = Omega_of,
"Ocean" = Omega_oc)
modelsummary(Omega_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'Final Omega model outputs (dropped factors)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | 0.005 | −0.004 | −0.007 | |
| (0.008) | (0.004) | (0.006) | ||
| Intercept | 3.656*** | −7.215 | 12.654 | 17.076 |
| (0.115) | (15.649) | (7.903) | (12.374) | |
| Upper Keys | 0.201*** | 0.150*** | 0.030 | |
| (0.056) | (0.041) | (0.020) | ||
| Middle Keys | 0.099+ | 0.059 | 0.024 | |
| (0.055) | (0.037) | (0.020) | ||
| Lower Keys | −0.297*** | −0.100** | −0.051** | |
| (0.055) | (0.036) | (0.019) | ||
| Num.Obs. | 347 | 319 | 388 | 148 |
| R2 Marg. | 0.129 | 0.084 | 0.038 | 0.003 |
| R2 Cond. | 0.594 | 0.468 | 0.542 | 0.740 |
| AIC | 309.6 | 32.4 | −420.9 | −138.1 |
| BIC | 332.7 | 58.8 | −393.2 | −126.1 |
| ICC | 0.5 | 0.4 | 0.5 | 0.7 |
| RMSE | 0.34 | 0.22 | 0.12 | 0.12 |
| + p |
modelsummary(Omega_models, output = "FLK_results/Figures/Fig_2/Trend_Omega.docx")
#modelsummary(Omega_models, estimate = "p.value")Trends_Omega<-ggarrange(In_Omega_Modeled+ theme(
#axis.text.x=element_blank(),
legend.position = "none"),
Mc_Omega_Modeled+ theme(legend.position = "none",
#axis.text.x=element_blank(),
axis.title.y=element_blank()),
Of_Omega_Modeled + theme(legend.position = "none",
#axis.text.x=element_blank(),
axis.title.y=element_blank()),
Oc_Omega_Modeled + theme(legend.position = "none",
#axis.text.x=element_blank(),
axis.title.y=element_blank()),
labels = c("C"),
ncol = 4, nrow = 1, widths =c(1.1, 1, 1, 1))
Trends_Omega
pCO2-All
##
## Shapiro-Wilk normality test
##
## data: Trends.data$pCO2_uatm
## W = 0.92391, p-value < 2.2e-16
## Model with all data
pCO2_all_o<-lmer(pCO2_uatm~Year * Zone * Sub_region + (1|Month)+
(1|SiteID), data=Trends.data)
step(Omega_all_o)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 33 -302.60 671.20
## (1 | SiteID) 1 32 -303.37 670.74 1.536 1 0.2153
## (1 | Month) 0 31 -453.42 968.83 300.097 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Zone:Sub_region 1 0.1701 0.02126 8 1161.0 0.2546 0.979753
## Year:Sub_region 2 0.1190 0.03967 3 1169.2 0.4775 0.698004
## Year:Zone 0 1.0532 0.35107 3 1172.0 4.2313 0.005506
## Zone:Sub_region 0 6.5766 0.82208 8 1172.0 9.9081 2.057e-13
##
## Year:Zone:Sub_region
## Year:Sub_region
## Year:Zone **
## Zone:Sub_region ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## Aragonite_Sat_W ~ Year + Zone + Sub_region + (1 | Month) + Year:Zone + Zone:Sub_regionpCO2_all_o_1<-lmer(pCO2_uatm ~ Year + Zone + Sub_region + (1 | Month) +
Year:Zone + Zone:Sub_region, data=Trends.data)
pCO2_all_o_3<-lmer(pCO2_uatm ~ Year + Zone + (1 | Month) + (1|Sub_region)+
Year:Zone, data=Trends.data)
anova(pCO2_all_o, pCO2_all_o_1, pCO2_all_o_3) summary(pCO2_all_o_1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pCO2_uatm ~ Year + Zone + Sub_region + (1 | Month) + Year:Zone +
## Zone:Sub_region
## Data: Trends.data
##
## REML criterion at convergence: 12892.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4226 -0.4951 -0.1069 0.4715 9.8228
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 1919 43.81
## Residual 2815 53.05
## Number of obs: 1202, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7923.307 3019.203 1181.115 2.624 0.008795
## Year -3.711 1.496 1181.096 -2.480 0.013271
## ZoneMid channel -6600.698 3984.938 1172.077 -1.656 0.097906
## ZoneOffshore -11827.038 3794.255 1172.164 -3.117 0.001871
## ZoneOceanic -17773.780 4984.880 1172.117 -3.566 0.000378
## Sub_regionUK -26.654 8.614 1172.009 -3.094 0.002021
## Sub_regionMK -30.269 8.469 1172.010 -3.574 0.000366
## Sub_regionLK 16.564 8.440 1172.062 1.963 0.049922
## Year:ZoneMid channel 3.262 1.975 1172.077 1.652 0.098827
## Year:ZoneOffshore 5.853 1.880 1172.164 3.113 0.001900
## Year:ZoneOceanic 8.790 2.470 1172.117 3.558 0.000388
## ZoneMid channel:Sub_regionUK 10.213 12.894 1172.019 0.792 0.428449
## ZoneOffshore:Sub_regionUK 19.779 12.133 1172.006 1.630 0.103317
## ZoneOceanic:Sub_regionUK 33.407 14.720 1172.033 2.270 0.023415
## ZoneMid channel:Sub_regionMK 19.871 12.000 1171.980 1.656 0.097997
## ZoneOffshore:Sub_regionMK 21.957 11.960 1171.980 1.836 0.066635
## ZoneOceanic:Sub_regionMK 46.300 12.288 1171.984 3.768 0.000173
## ZoneMid channel:Sub_regionLK -9.327 11.950 1171.977 -0.781 0.435242
## ZoneOffshore:Sub_regionLK -16.100 11.628 1171.981 -1.385 0.166457
##
## (Intercept) **
## Year *
## ZoneMid channel .
## ZoneOffshore **
## ZoneOceanic ***
## Sub_regionUK **
## Sub_regionMK ***
## Sub_regionLK *
## Year:ZoneMid channel .
## Year:ZoneOffshore **
## Year:ZoneOceanic ***
## ZoneMid channel:Sub_regionUK
## ZoneOffshore:Sub_regionUK
## ZoneOceanic:Sub_regionUK *
## ZoneMid channel:Sub_regionMK .
## ZoneOffshore:Sub_regionMK .
## ZoneOceanic:Sub_regionMK ***
## ZoneMid channel:Sub_regionLK
## ZoneOffshore:Sub_regionLK
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficient anova(pCO2_all_o_1) ranova(pCO2_all_o_1)# Obtain slopes
ranef(pCO2_all_o_1)## $Month
## (Intercept)
## Apr -58.142133
## May -55.254247
## Jun 3.487297
## Jul 42.308538
## Aug 38.627165
## Sep 59.772212
## Oct 50.461554
## Nov 20.226263
## Dec 2.741919
## Jan -10.876983
## Feb -45.762902
## Mar -47.588682
##
## with conditional variances for "Month" fixef(pCO2_all_o_1)## (Intercept) Year
## 7923.306666 -3.710778
## ZoneMid channel ZoneOffshore
## -6600.698165 -11827.038267
## ZoneOceanic Sub_regionUK
## -17773.780213 -26.654277
## Sub_regionMK Sub_regionLK
## -30.268537 16.564376
## Year:ZoneMid channel Year:ZoneOffshore
## 3.262160 5.852538
## Year:ZoneOceanic ZoneMid channel:Sub_regionUK
## 8.790155 10.213277
## ZoneOffshore:Sub_regionUK ZoneOceanic:Sub_regionUK
## 19.779107 33.406978
## ZoneMid channel:Sub_regionMK ZoneOffshore:Sub_regionMK
## 19.871245 21.956598
## ZoneOceanic:Sub_regionMK ZoneMid channel:Sub_regionLK
## 46.299702 -9.327342
## ZoneOffshore:Sub_regionLK
## -16.099874 coef(pCO2_all_o_1)$Month coef(pCO2_all_o_1)$Zone## NULL m.lst <- lstrends(pCO2_all_o_1, "Zone", var="Year")
m.lst## Zone Year.trend SE df lower.CL upper.CL
## Inshore -3.711 1.50 1181 -6.649 -0.772
## Mid channel -0.449 1.54 1181 -3.477 2.580
## Offshore 2.142 1.42 1181 -0.639 4.923
## Oceanic 5.079 2.14 1177 0.884 9.275
##
## Results are averaged over the levels of: Sub_region
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95 pairs(m.lst)## contrast estimate SE df t.ratio p.value
## Inshore - Mid channel -3.26 1.97 1172 -1.652 0.3500
## Inshore - Offshore -5.85 1.88 1172 -3.113 0.0102
## Inshore - Oceanic -8.79 2.47 1172 -3.558 0.0022
## Mid channel - Offshore -2.59 1.92 1172 -1.351 0.5306
## Mid channel - Oceanic -5.53 2.50 1172 -2.213 0.1204
## Offshore - Oceanic -2.94 2.42 1172 -1.212 0.6196
##
## Results are averaged over the levels of: Sub_region
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 4 estimatesInshore
# Inshore pCO2
hist(Trends.inshore$pCO2_uatm)
shapiro.test(Trends.inshore$pCO2_uatm)# Normality NOT? rejected##
## Shapiro-Wilk normality test
##
## data: Trends.inshore$pCO2_uatm
## W = 0.97119, p-value = 2.168e-06 pCO2_in_0<-lmer(pCO2_uatm~Year * Sub_region + (1|Month)+
#(1|Zone)+
(1|SiteID), data=Trends.inshore)
#summary(pCO2_in_0)
anova(pCO2_in_0) ranova(pCO2_in_0) step(pCO2_in_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1960.9 3943.7
## (1 | Month) 0 10 -2082.9 4185.9 244.167 1 < 2e-16 ***
## (1 | SiteID) 0 10 -1964.4 3948.8 7.104 1 0.00769 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 14407 4802.2 3 321.29 1.0116 0.3877
## Year 2 2648 2648.5 1 334.11 0.5577 0.4557
## Sub_region 3 37312 12437.2 3 7.00 2.6243 0.1323
##
## Model found:
## pCO2_uatm ~ (1 | Month) + (1 | SiteID)pCO2_in<-lmer(pCO2_uatm ~ (1|Month)+(1|SiteID), data=Trends.inshore)
summary(pCO2_in)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pCO2_uatm ~ (1 | Month) + (1 | SiteID)
## Data: Trends.inshore
##
## REML criterion at convergence: 3972.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5290 -0.6081 -0.0745 0.5398 7.1829
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 7029.7 83.84
## SiteID (Intercept) 568.5 23.84
## Residual 4738.5 68.84
## Number of obs: 347, groups: Month, 12; SiteID, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 425.68 25.55 12.80 16.66 4.71e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 anova(pCO2_in) plot(pCO2_in)
qqnorm(resid(pCO2_in))
acf(resid(pCO2_in), main="acf(resid(pCO2 Inshore))") 
#pCO2_in.emmc<-emmeans(pCO2_in, ~Month)
#pCO2_in.emmc<-multcomp::cld(pCO2_in.emmc)
#pCO2_in.emmc<-pCO2_in.emmc[order(pCO2_in.emmc$Sub_region),]
#pCO2_in.emmc# Predict values
In_pCO2_Pred <- predict(pCO2_in, newdata=In_Newdata, re.form=NA, level=0, asList = FALSE)
In_pCO2_Pred <-cbind(In_Newdata, In_pCO2_Pred)
In_pCO2_Pred$Date<-as.Date(as.character(In_pCO2_Pred$Year), format = "%Y")
# # Model summary
# slope<-round(
# summary(pCO2_in)$coefficients["Year", "Estimate"], digits = 3)
# xs <- as.Date(as.character("2018-11-20"))
# ys <- max(Trends.midchanel$pCO2_uatm, na.rm =T)
#
# # Plot raw data point and fitted model
# plot_label <- sprintf("\"\" ~ pCO[2] == %0.3f", slope)
# plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
In_pCO2_Modeled<-Long_plot2+ facet_grid(~Zone)+
scale_y_continuous(name=expression(paste(pCO[2], ~" (", mu, " atm)")),
limits = c(200,800),
breaks = seq(0, 800, 100),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.inshore,
aes(y=pCO2_uatm, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=21)+
geom_line(data=In_pCO2_Pred, aes(y = In_pCO2_Pred,
x=Date), linetype=2, size=1, colour = "black")
In_pCO2_Modeled
Mid channel
hist(Trends.midchanel$pCO2_uatm)
shapiro.test(Trends.midchanel$pCO2_uatm)# Normality NOT rejected##
## Shapiro-Wilk normality test
##
## data: Trends.midchanel$pCO2_uatm
## W = 0.99101, p-value = 0.04862pCO2_mc_0<-lmer(pCO2_uatm~Year * Sub_region + (1|Month) +
#(1|Zone)+
(1|SiteID), data=Trends.midchanel)
#summary(pCO2_mc_0)
anova(pCO2_mc_0) ranova(pCO2_mc_0) step(pCO2_mc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1602.9 3227.7
## (1 | SiteID) 1 10 -1603.6 3227.2 1.453 1 0.228
## (1 | Month) 0 9 -1706.5 3431.0 205.873 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 5080.2 1693.4 3 300.03 1.2392 0.2956304
## Year 2 409.9 409.9 1 313.01 0.2993 0.5847329
## Sub_region 0 27356.2 9118.7 3 304.06 6.6755 0.0002232 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## pCO2_uatm ~ Sub_region + (1 | Month)pCO2_mc<-lmer(pCO2_uatm ~ Year + Sub_region + (1 | Month),
data = Trends.midchanel)
summary(pCO2_mc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pCO2_uatm ~ Year + Sub_region + (1 | Month)
## Data: Trends.midchanel
##
## REML criterion at convergence: 3222
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0752 -0.6290 -0.1366 0.6270 4.0314
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 1815 42.60
## Residual 1370 37.01
## Number of obs: 319, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1822.0813 2568.2119 313.0123 0.709 0.4786
## Year -0.6962 1.2727 313.0065 -0.547 0.5847
## Sub_regionUK -16.9020 6.6997 303.0578 -2.523 0.0122 *
## Sub_regionMK -9.9623 5.9415 303.0405 -1.677 0.0946 .
## Sub_regionLK 7.5901 5.9317 303.1191 1.280 0.2017
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.020 0.019
## Sub_reginMK -0.004 0.003 0.530
## Sub_reginLK -0.026 0.024 0.533 0.602 anova(pCO2_mc) plot(pCO2_mc)
qqnorm(resid(pCO2_mc))
acf(resid(pCO2_mc), main="acf(resid(pCO2 Mid Channel))") 
pCO2_mc.emmc<-emmeans(pCO2_mc, ~Sub_region+Year)
pCO2_mc.emmc<-multcomp::cld(pCO2_mc.emmc)
pCO2_mc.emmc<-pCO2_mc.emmc[order(
pCO2_mc.emmc$Sub_region),]
pCO2_mc.emmc #write.csv(pCO2_mc.emmc, "FLK_results/Model_2_LTpCO2_POST.csv")# Predict values
Mc_pCO2_Pred <- predict(pCO2_mc, newdata=Mc_Newdata, re.form=NA, level=0, asList = FALSE)
Mc_pCO2_Pred <-cbind(Mc_Newdata, Mc_pCO2_Pred)
Mc_pCO2_Pred$Date<-as.Date(as.character(Mc_pCO2_Pred$Year), format = "%Y")
# Model summary
slope<-round(
summary(pCO2_mc)$coefficients["Year", "Estimate"], digits = 3)
#ys <- max(Trends.inshore$pCO2_uatm, na.rm =T)
ys <- max(Trends.midchanel$pCO2_uatm, na.rm =T)+50
# Plot raw data point and fitted model
plot_label <- sprintf("\"\" ~ pCO[2] == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Mc_pCO2_Modeled<-Long_plot2+ facet_grid(~Zone)+
scale_y_continuous(name=expression(paste(pCO[2], ~" (", mu, " atm)")),
limits = c(200,800),
breaks = seq(0, 800, 100),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.midchanel,
aes(y=pCO2_uatm, x=Date, #fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=23)+
geom_line(data=Mc_pCO2_Pred, aes(y = Mc_pCO2_Pred,
x=Date, colour = Sub_region),
linetype=1, size=1) +
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
#label = expression("~Omega[Ar] == -0.015** ~Year ^-1"),
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Mc_pCO2_Modeled
Offshore
hist(Trends.offshore$pCO2_uatm)
shapiro.test(Trends.offshore$pCO2_uatm)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.offshore$pCO2_uatm
## W = 0.98049, p-value = 4.25e-05pCO2_of_0<-lmer(pCO2_uatm~Year * Sub_region +
(1|Month) + (1|SiteID),
data=Trends.offshore)
#summary(pCO2_of_0)
anova(pCO2_of_0) ranova(pCO2_of_0) step(pCO2_of_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1754.3 3530.6
## (1 | SiteID) 1 10 -1754.3 3528.6 0.00 1 1
## (1 | Month) 0 9 -1888.3 3794.7 268.04 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 0 4488 1496 3 368.87 3.1239 0.02594 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## pCO2_uatm ~ Year + Sub_region + (1 | Month) + Year:Sub_regionpCO2_of<-lmer(pCO2_uatm ~ Year + Sub_region + (1 | Month),
data =Trends.offshore)
summary(pCO2_of)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pCO2_uatm ~ Year + Sub_region + (1 | Month)
## Data: Trends.offshore
##
## REML criterion at convergence: 3525.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8917 -0.6612 -0.0745 0.5456 5.1171
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 792.6 28.15
## Residual 487.1 22.07
## Number of obs: 388, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2868.7304 1388.0888 381.0773 -2.067 0.0394 *
## Year 1.6282 0.6879 381.0643 2.367 0.0184 *
## Sub_regionUK -5.4089 3.5549 371.8480 -1.522 0.1290
## Sub_regionMK -8.5154 3.5165 371.8470 -2.422 0.0159 *
## Sub_regionLK 2.0882 3.3455 371.9472 0.624 0.5329
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.008 0.006
## Sub_reginMK -0.007 0.006 0.590
## Sub_reginLK -0.021 0.019 0.622 0.630 anova(pCO2_of) plot(pCO2_of)
qqnorm(resid(pCO2_of))
acf(resid(Omega_in), main="acf(resid(pCO2 Offshore))") 
pCO2_of.emmc<-emmeans(pCO2_of, ~Sub_region)
pCO2_of.emmc<-multcomp::cld(pCO2_of.emmc)
pCO2_of.emmc<-pCO2_of.emmc[order(pCO2_of.emmc$Sub_region),]
pCO2_of.emmc #write.csv(pCO2_of.emmc, "FLK_results/Model_3_LTpCO2_POST.csv")# Predict values
Of_pCO2_Pred <- predict(pCO2_of, newdata=Of_Newdata, re.form=NA, level=0, asList = FALSE)
Of_pCO2_Pred <-cbind(Of_Newdata, Of_pCO2_Pred)
Of_pCO2_Pred$Date<-as.Date(as.character(Of_pCO2_Pred$Year), format = "%Y")
# Model summary for slope labels
slope<-round(
summary(pCO2_of)$coefficients["Year", "Estimate"], digits = 3)
#xs <- as.Date(as.character("2018-11-20"))
ys <- max(Trends.offshore$pCO2_uatm, na.rm =T) -15
plot_label <- sprintf("\"\" ~ pCO[2] == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Of_pCO2_Modeled<-Long_plot2+ facet_grid(~Zone)+
scale_y_continuous(name=expression(paste(pCO[2], ~" (", mu, " atm)")),
limits = c(300,605),
breaks = seq(0, 800, 100),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.offshore,
aes(y=pCO2_uatm, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=24)+
geom_line(data=Of_pCO2_Pred, aes(y = Of_pCO2_Pred,
x=Date, colour = Sub_region),
linetype=1, size=1) +
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
#label = expression("~Omega[Ar] == -0.015** ~Year ^-1"),
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Of_pCO2_Modeled
Oceanic
hist(Trends.ocean$pCO2_uatm)
shapiro.test(Trends.ocean$pCO2_uatm)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.ocean$pCO2_uatm
## W = 0.88517, p-value = 2.506e-09pCO2_oc_0<-lmer(pCO2_uatm ~ Year * Sub_region +
(1 | Month)+ (1|SiteID),
data=Trends.ocean)
#summary(pCO2_oc_0)
anova(pCO2_oc_0) ranova(pCO2_oc_0) step(pCO2_oc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 9 -682.07 1382.1
## (1 | SiteID) 1 8 -682.07 1380.1 0.000 1 0.9999
## (1 | Month) 0 7 -696.67 1407.3 29.202 1 6.522e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 2542.1 1271.05 2 132.19 2.0233 0.1363
## Sub_region 2 1046.3 523.14 2 134.28 0.8205 0.4424
## Year 3 2388.0 2388.03 1 142.33 3.7529 0.0547 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## pCO2_uatm ~ (1 | Month)pCO2_oc<-lmer(pCO2_uatm ~ Year + (1 | Month),
data = Trends.ocean)
summary(pCO2_oc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pCO2_uatm ~ Year + (1 | Month)
## Data: Trends.ocean
##
## REML criterion at convergence: 1387
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0803 -0.5007 -0.0498 0.4464 7.2224
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 250.2 15.82
## Residual 636.3 25.23
## Number of obs: 148, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4213.683 2389.335 142.325 -1.764 0.0800 .
## Year 2.294 1.184 142.329 1.937 0.0547 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Year -1.000 anova(pCO2_oc) plot(pCO2_oc)
qqnorm(resid(pCO2_oc))
acf(resid(pCO2_oc), main="acf(resid(pCO2 open))")
pCO2_oc.emmc<-emmeans(pCO2_oc, ~Year)
pCO2_oc.emmc<-multcomp::cld(pCO2_oc.emmc)
#pCO2_oc.emmc<-pCO2_oc.emmc[order(pCO2_oc.emmc$Sub_region),]
pCO2_oc.emmc# Predict values
Oc_pCO2_Pred <- predict(pCO2_oc, newdata=Oc_Newdata,
re.form=NA, level=0, asList = FALSE)
Oc_pCO2_Pred <-cbind(Oc_Newdata, Oc_pCO2_Pred)
Oc_pCO2_Pred$Date<-as.Date(as.character(Oc_pCO2_Pred$Year), format = "%Y")
# Model summary for slope labels
slope<-round(
summary(pCO2_oc)$coefficients["Year", "Estimate"], digits = 3)
#xs <- as.Date(as.character("2018-11-20"))
ys <- max(Trends.offshore$pCO2_uatm, na.rm =T)-15
plot_label <- sprintf("\"\" ~ pCO[2] == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Oc_pCO2_Modeled<-Long_plot2+ facet_grid(~Zone)+
scale_y_continuous(name=expression(paste(pCO[2], ~" (", mu, " atm)")),
limits = c(300,605),
breaks = seq(0, 800, 100),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.ocean,
aes(y=pCO2_uatm, x=Date, #fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=25)+
geom_line(data=Oc_pCO2_Pred, aes(y = Oc_pCO2_Pred, x=Date),
colour = "black", linetype=1, size=1) +
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Oc_pCO2_Modeled
Models summary
Complete models
# ANOVAs
# Convert ANOVA results into dataframes allows for easier name manipulation
av.pCO2_in_0 <- data.frame (anova(pCO2_in_0))
av.pCO2_mc_0 <- data.frame (anova(pCO2_mc_0) )
av.pCO2_of_0 <- data.frame (anova(pCO2_of_0))
av.pCO2_oc_0 <- data.frame (anova(pCO2_oc_0))
# Putting all into one dataframe/table
anova_pH <- data.frame(cbind
(c("Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region"),
rbind(av.pCO2_in_0, av.pCO2_mc_0,
av.pCO2_of_0, av.pCO2_oc_0)))
colnames(anova_pH) <- c("", "Sum Sq", "Mean Sq", "N Df" , "D Df",
"F value", "Pr(>F)")
row.names(anova_pH) <- NULL
# create HTML table using kableExtra
anova_pH %>% kable("html", digits=2) %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "pCO2 inshore", 1, 3) %>% # groups rows with label
pack_rows(., "pCO2 mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "pCO2 offshore", 7, 9) %>% # groups rows with label
pack_rows(., "pCO2 ocean", 10, 12) | Sum Sq | Mean Sq | N Df | D Df | F value | Pr(>F) | |
|---|---|---|---|---|---|---|
| pCO2 inshore | ||||||
| Year | 2463.70 | 2463.70 | 1 | 331.01 | 0.52 | 0.47 |
| Sub_region | 14472.40 | 4824.13 | 3 | 321.29 | 1.02 | 0.39 |
| Year:Sub_region | 14406.68 | 4802.23 | 3 | 321.29 | 1.01 | 0.39 |
| pCO2 mid-channel | ||||||
| Year | 279.44 | 279.44 | 1 | 304.56 | 0.21 | 0.65 |
| Sub_region | 5172.46 | 1724.15 | 3 | 294.68 | 1.28 | 0.28 |
| Year:Sub_region | 5153.88 | 1717.96 | 3 | 294.68 | 1.28 | 0.28 |
| pCO2 offshore | ||||||
| Year | 3634.86 | 3634.86 | 1 | 377.77 | 7.59 | 0.01 |
| Sub_region | 4496.27 | 1498.76 | 3 | 368.89 | 3.13 | 0.03 |
| Year:Sub_region | 4488.04 | 1496.01 | 3 | 368.89 | 3.12 | 0.03 |
| pCO2 ocean | ||||||
| Year | 1298.46 | 1298.46 | 1 | 140.16 | 2.07 | 0.15 |
| Sub_region | 2540.18 | 1270.09 | 2 | 132.19 | 2.02 | 0.14 |
| Year:Sub_region | 2542.11 | 1271.05 | 2 | 132.19 | 2.02 | 0.14 |
# R-ANOVAs
rav.pCO2_in_0 <- data.frame (ranova(pCO2_in_0))
rav.pCO2_mc_0 <- data.frame (ranova(pCO2_mc_0) )
rav.pCO2_of_0 <- data.frame (ranova(pCO2_of_0))
rav.pCO2_oc_0 <- data.frame (ranova(pCO2_oc_0))
# Putting all into one dataframe/table
ranova_pCO2 <- data.frame(cbind
(c("<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)"),
rbind(rav.pCO2_in_0, rav.pCO2_mc_0,
rav.pCO2_of_0, rav.pCO2_oc_0)))
colnames(ranova_pCO2) <- c("", "npar", "logLik", "AIC" , "LRT",
"DF", "Pr(>Chi Sq)")
row.names(ranova_pCO2) <- NULL
# create HTML table using kableExtra
ranova_pCO2 %>% kable("html", digits=2) %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Omega inshore", 1, 3) %>% # groups rows with label
pack_rows(., "Omega mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "Omega offshore", 7, 9) %>% # groups rows with label
pack_rows(., "Omega ocean", 10, 12) | npar | logLik | AIC | LRT | DF | Pr(>Chi Sq) | |
|---|---|---|---|---|---|---|
| Omega inshore | ||||||
| <none> | 11 | -1960.86 | 3943.72 | |||
| (1 | Month) | 10 | -2082.94 | 4185.89 | 244.17 | 1 | 0.00 |
| (1 | SiteID) | 10 | -1964.41 | 3948.82 | 7.10 | 1 | 0.01 |
| Omega mid-channel | ||||||
| <none> | 11 | -1602.86 | 3227.71 | |||
| (1 | Month) | 10 | -1706.52 | 3433.04 | 207.33 | 1 | 0.00 |
| (1 | SiteID) | 10 | -1603.58 | 3227.17 | 1.45 | 1 | 0.23 |
| Omega offshore | ||||||
| <none> | 11 | -1754.32 | 3530.63 | |||
| (1 | Month) | 10 | -1888.34 | 3796.67 | 268.04 | 1 | 0.00 |
| (1 | SiteID) | 10 | -1754.32 | 3528.63 | 0.00 | 1 | 1.00 |
| Omega ocean | ||||||
| <none> | 9 | -682.07 | 1382.13 | |||
| (1 | Month) | 8 | -696.67 | 1409.33 | 29.20 | 1 | 0.00 |
| (1 | SiteID) | 8 | -682.07 | 1380.13 | 0.00 | 1 | 1.00 |
pCO2_models_0<-list("Inshore" = pCO2_in_0,
"Mid channel" = pCO2_mc_0,
"Offshore" = pCO2_of_0,
"Ocean" = pCO2_oc_0)
modelsummary(pCO2_models_0, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'pCO model outputs (all factors)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | 0.297 | 0.493 | 3.299* | 0.261 |
| (4.249) | (2.297) | (1.359) | (2.372) | |
| Intercept | −163.198 | −578.151 | −6239.564* | −117.276 |
| (8573.324) | (4634.475) | (2741.383) | (4786.602) | |
| Upper Keys | 4386.682 | 1146.061 | 3881.748 | |
| (10859.179) | (6354.925) | (3388.676) | ||
| Middle Keys | −2246.265 | −806.335 | −181.767 | −8472.658 |
| (10523.741) | (5622.170) | (3356.720) | (5480.976) | |
| Lower Keys | 13637.873 | 8339.327 | 7481.548* | −728.455 |
| (10602.918) | (5677.101) | (3199.251) | (5563.983) | |
| Num.Obs. | 347 | 319 | 388 | 148 |
| R2 Marg. | 0.032 | 0.031 | 0.032 | 0.071 |
| R2 Cond. | 0.616 | 0.593 | 0.635 | |
| AIC | 3943.7 | 3227.7 | 3530.6 | 1382.1 |
| BIC | 3986.1 | 3269.1 | 3574.2 | 1409.1 |
| ICC | 0.6 | 0.6 | 0.6 | |
| RMSE | 66.52 | 35.40 | 21.35 | 23.77 |
| + p |
#modelsummary(Omega_models, estimate = "p.value")Factors removed models
pCO2_models<-list("Inshore" = pCO2_in,
"Mid channel" = pCO2_mc,
"Offshore" = pCO2_of,
"Ocean" = pCO2_oc)
modelsummary(pCO2_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'pCO2 models outputs (dropped factors)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | −0.696 | 1.628* | 2.294+ | |
| (1.273) | (0.688) | (1.184) | ||
| Intercept | 425.681*** | 1822.081 | −2868.730* | −4213.683+ |
| (25.549) | (2568.212) | (1388.089) | (2389.335) | |
| Upper Keys | −16.902* | −5.409 | ||
| (6.700) | (3.555) | |||
| Middle Keys | −9.962+ | −8.515* | ||
| (5.942) | (3.516) | |||
| Lower Keys | 7.590 | 2.088 | ||
| (5.932) | (3.346) | |||
| Num.Obs. | 347 | 319 | 388 | 148 |
| R2 Marg. | 0.000 | 0.027 | 0.024 | 0.026 |
| R2 Cond. | 0.616 | 0.581 | 0.628 | 0.301 |
| AIC | 3980.8 | 3236.0 | 3539.3 | 1395.0 |
| BIC | 3996.2 | 3262.3 | 3567.1 | 1407.0 |
| ICC | 0.6 | 0.6 | 0.6 | 0.3 |
| RMSE | 66.86 | 36.09 | 21.62 | 24.28 |
| + p |
#modelsummary(pCO2_models, estimate = "p.value")Trends_pCO2<-ggarrange(In_pCO2_Modeled+ theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
legend.position = "none"),
Mc_pCO2_Modeled + theme(legend.position = "none",
axis.title.y=element_blank(),
axis.text.x=element_blank()),
Of_pCO2_Modeled + theme(legend.position = "none",
axis.title.y=element_blank(),
axis.text.x=element_blank()),
Oc_pCO2_Modeled + theme(legend.position = "none",
axis.title.y=element_blank(),
axis.text.x=element_blank()),
labels = c("A"),
ncol = 4, nrow = 1, widths =c(1.1, 1, 1, 1))
Trends_pCO2
pH-All
##
## Shapiro-Wilk normality test
##
## data: Trends.data$pH_calculated
## W = 0.95916, p-value < 2.2e-16
## Model with all data
pH_all_o<-lmer(pH_calculated ~ Year * Zone * Sub_region + (1|Month)+
(1|SiteID), data=Trends.data)
step(pH_all_o)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 33 1912.5 -3759.0
## (1 | Month) 0 32 1592.7 -3121.3 639.67 1 < 2e-16 ***
## (1 | SiteID) 0 32 1910.5 -3757.1 3.89 1 0.04871 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value
## Year:Zone:Sub_region 1 0.001540 0.0001925 8 1139.90 0.1031
## Year:Sub_region 2 0.012630 0.0042099 3 1147.15 2.2692
## Year:Zone 0 0.037848 0.0126161 3 1149.97 6.7767
## Zone:Sub_region 0 0.044190 0.0055237 8 23.65 2.9671
## Pr(>F)
## Year:Zone:Sub_region 0.9991245
## Year:Sub_region 0.0788897 .
## Year:Zone 0.0001572 ***
## Zone:Sub_region 0.0188489 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## pH_calculated ~ Year + Zone + Sub_region + (1 | Month) + (1 | SiteID) + Year:Zone + Zone:Sub_regionpH_all_o_1<-lmer(pH_calculated ~ Year + Zone + Sub_region + (1 | Month) +
Year:Zone + Zone:Sub_region, data=Trends.data)
pH_all_o_3<-lmer(pH_calculated ~ Year + Zone + (1 | Month) + (1|Sub_region)+
Year:Zone, data=Trends.data)
anova(pH_all_o, pH_all_o_1, pH_all_o_3) summary(pH_all_o_1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pH_calculated ~ Year + Zone + Sub_region + (1 | Month) + Year:Zone +
## Zone:Sub_region
## Data: Trends.data
##
## REML criterion at convergence: -3921.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.5800 -0.5213 0.0656 0.4949 4.6575
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.001768 0.04205
## Residual 0.001887 0.04344
## Number of obs: 1202, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.367e+00 2.474e+00 1.179e+03 0.552 0.580764
## Year 3.302e-03 1.226e-03 1.179e+03 2.693 0.007178
## ZoneMid channel 5.957e+00 3.263e+00 1.172e+03 1.826 0.068141
## ZoneOffshore 1.094e+01 3.107e+00 1.172e+03 3.522 0.000445
## ZoneOceanic 1.594e+01 4.082e+00 1.172e+03 3.906 9.92e-05
## Sub_regionUK 2.424e-02 7.054e-03 1.172e+03 3.437 0.000609
## Sub_regionMK 1.728e-02 6.934e-03 1.172e+03 2.492 0.012823
## Sub_regionLK -2.795e-02 6.911e-03 1.172e+03 -4.045 5.57e-05
## Year:ZoneMid channel -2.948e-03 1.617e-03 1.172e+03 -1.823 0.068527
## Year:ZoneOffshore -5.419e-03 1.540e-03 1.172e+03 -3.519 0.000449
## Year:ZoneOceanic -7.883e-03 2.023e-03 1.172e+03 -3.897 0.000103
## ZoneMid channel:Sub_regionUK -8.027e-03 1.056e-02 1.172e+03 -0.760 0.447201
## ZoneOffshore:Sub_regionUK -1.935e-02 9.934e-03 1.172e+03 -1.948 0.051645
## ZoneOceanic:Sub_regionUK -4.318e-02 1.205e-02 1.172e+03 -3.582 0.000354
## ZoneMid channel:Sub_regionMK -1.081e-02 9.826e-03 1.172e+03 -1.100 0.271552
## ZoneOffshore:Sub_regionMK -1.211e-02 9.793e-03 1.172e+03 -1.236 0.216535
## ZoneOceanic:Sub_regionMK -4.395e-02 1.006e-02 1.172e+03 -4.368 1.37e-05
## ZoneMid channel:Sub_regionLK 1.680e-02 9.785e-03 1.172e+03 1.717 0.086267
## ZoneOffshore:Sub_regionLK 2.607e-02 9.522e-03 1.172e+03 2.738 0.006281
##
## (Intercept)
## Year **
## ZoneMid channel .
## ZoneOffshore ***
## ZoneOceanic ***
## Sub_regionUK ***
## Sub_regionMK *
## Sub_regionLK ***
## Year:ZoneMid channel .
## Year:ZoneOffshore ***
## Year:ZoneOceanic ***
## ZoneMid channel:Sub_regionUK
## ZoneOffshore:Sub_regionUK .
## ZoneOceanic:Sub_regionUK ***
## ZoneMid channel:Sub_regionMK
## ZoneOffshore:Sub_regionMK
## ZoneOceanic:Sub_regionMK ***
## ZoneMid channel:Sub_regionLK .
## ZoneOffshore:Sub_regionLK **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficient anova(pH_all_o_1) ranova(pH_all_o_1) ranef(pH_all_o_1)## $Month
## (Intercept)
## Apr 0.052537644
## May 0.046227147
## Jun -0.013039323
## Jul -0.047433841
## Aug -0.040070419
## Sep -0.057214875
## Oct -0.040240854
## Nov -0.016435554
## Dec 0.001651854
## Jan 0.015533741
## Feb 0.052333930
## Mar 0.046150550
##
## with conditional variances for "Month" fixef(pH_all_o_1)## (Intercept) Year
## 1.366691432 0.003301801
## ZoneMid channel ZoneOffshore
## 5.957402352 10.941634460
## ZoneOceanic Sub_regionUK
## 15.943021621 0.024243735
## Sub_regionMK Sub_regionLK
## 0.017283650 -0.027954363
## Year:ZoneMid channel Year:ZoneOffshore
## -0.002948182 -0.005418733
## Year:ZoneOceanic ZoneMid channel:Sub_regionUK
## -0.007883005 -0.008027417
## ZoneOffshore:Sub_regionUK ZoneOceanic:Sub_regionUK
## -0.019352940 -0.043178165
## ZoneMid channel:Sub_regionMK ZoneOffshore:Sub_regionMK
## -0.010808465 -0.012108819
## ZoneOceanic:Sub_regionMK ZoneMid channel:Sub_regionLK
## -0.043949576 0.016799753
## ZoneOffshore:Sub_regionLK
## 0.026067218 coef(pH_all_o_1)$Month coef(pH_all_o_1)$Zone## NULL m.lst <- lstrends(pH_all_o_1, "Zone", var="Year")
m.lst## Zone Year.trend SE df lower.CL upper.CL
## Inshore 0.003302 0.00123 1179 0.000895 0.005709
## Mid channel 0.000354 0.00126 1179 -0.002127 0.002834
## Offshore -0.002117 0.00116 1180 -0.004395 0.000161
## Oceanic -0.004581 0.00175 1176 -0.008017 -0.001145
##
## Results are averaged over the levels of: Sub_region
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95 pairs(m.lst)## contrast estimate SE df t.ratio p.value
## Inshore - Mid channel 0.00295 0.00162 1172 1.823 0.2628
## Inshore - Offshore 0.00542 0.00154 1172 3.519 0.0025
## Inshore - Oceanic 0.00788 0.00202 1172 3.897 0.0006
## Mid channel - Offshore 0.00247 0.00157 1172 1.574 0.3942
## Mid channel - Oceanic 0.00493 0.00205 1172 2.412 0.0753
## Offshore - Oceanic 0.00246 0.00199 1172 1.241 0.6007
##
## Results are averaged over the levels of: Sub_region
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 4 estimates # Create a new data frame for independent variables
Newdata<-expand.grid(Zone=unique(Trends.data$Zone),
Sub_region=unique(Trends.data$Sub_region),
Year=seq((min(Trends.data$Year)-0), (max(Trends.data$Year)+1)))
# Predict values
pH_Pred <- predict(pH_all_o_1, newdata=Newdata,
re.form=NA, level=0, asList = FALSE)
pH_Pred <-cbind(Newdata, pH_Pred)
pH_Pred$Date<-as.Date(as.character(pH_Pred$Year), format = "%Y")
pH_Modeled<-Long_plot2+ facet_grid(~Zone)+
scale_y_continuous(name=(expression(pH[calculated])),
limits=c(7.80, 8.28),
breaks = seq(0, 10, 0.1),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.data,
aes(y=pH_calculated, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5,shape=21)+
geom_line(data=pH_Pred, aes(y = pH_Pred, x=Date, colour = Sub_region),
linetype=2, size=1) #+
#annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
# label =paste0(as.character(plot_label, plot_label2, sep = " ")),
# size=4)
pH_Modeled
Inshore
# Inshore pH
hist(Trends.inshore$pH_calculated)
shapiro.test(Trends.inshore$pH_calculated)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.inshore$pH_calculated
## W = 0.99359, p-value = 0.1483pH_in_0<-lmer(pH_calculated~Year * Sub_region + (1|Month)+
#(1|Zone)+
(1|SiteID),
data=Trends.inshore)
#summary(pH_in_0)
anova(pH_in_0) ranova(pH_in_0) step(pH_in_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 464.47 -906.94
## (1 | Month) 0 10 316.60 -613.19 295.746 1 < 2e-16 ***
## (1 | SiteID) 0 10 461.43 -902.87 6.067 1 0.01377 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 0.006900 0.0022998 3 321.24 0.7999 0.49464
## Year 2 0.000653 0.0006535 1 332.54 0.2276 0.63361
## Sub_region 0 0.046162 0.0153874 3 7.01 5.3752 0.03095 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## pH_calculated ~ Sub_region + (1 | Month) + (1 | SiteID)pH_in<-lmer(pH_calculated ~ Sub_region + (1 | Month) + (1 | SiteID),
data=Trends.inshore)
summary(pH_in) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pH_calculated ~ Sub_region + (1 | Month) + (1 | SiteID)
## Data: Trends.inshore
##
## REML criterion at convergence: -965.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.8544 -0.6181 0.0023 0.5730 3.7735
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.0057552 0.07586
## SiteID (Intercept) 0.0001789 0.01337
## Residual 0.0028627 0.05350
## Number of obs: 347, groups: Month, 12; SiteID, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.02899 0.02476 15.41449 324.269 <2e-16 ***
## Sub_regionUK 0.02212 0.01499 7.00533 1.475 0.184
## Sub_regionMK 0.01751 0.01491 6.85157 1.175 0.279
## Sub_regionLK -0.02659 0.01489 6.83100 -1.785 0.118
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sb_rUK Sb_rMK
## Sub_reginUK -0.357
## Sub_reginMK -0.359 0.592
## Sub_reginLK -0.359 0.592 0.596 anova(pH_in) plot(pH_in)
qqnorm(resid(pH_in))
acf(resid(pH_in), main="acf(resid(pH Inshore))") 
pH_in.emmc<-emmeans(pH_in, ~Sub_region)
pH_in.emmc<-multcomp::cld(pH_in.emmc)
pH_in.emmc<-pH_in.emmc[order(pH_in.emmc$Sub_region),]
pH_in.emmc#write.csv(pH_in.emmc, "FLK_results/Model_5_LTpH_POST.csv")# Predict values
In_pH_Pred <- predict(pH_in, newdata=In_Newdata,
re.form=NA, level=0, asList = FALSE)
In_pH_Pred <-cbind(In_Newdata, In_pH_Pred)
In_pH_Pred$Date<-as.Date(as.character(In_pH_Pred$Year), format = "%Y")
# Model summary for slope labels
# slope<-round(
# summary(pH_in)$coefficients["Year", "Estimate"], digits = 3)
# xs <- as.Date(as.character("2018-11-20"))
# ys <- max(Trends.inshore$pH_calculated, na.rm =T)
# plot_label <- sprintf("\"\" ~ pH == %0.3f", slope)
# plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
In_pH_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(name=(expression(pH[calculated])),
limits=c(7.80, 8.28),
breaks = seq(0, 10, 0.1),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.inshore,
aes(y=pH_calculated, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5,shape=21)+
geom_line(data=In_pH_Pred, aes(y = In_pH_Pred, x=Date, colour = Sub_region),
linetype=2, size=1) #+
#annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
# label =paste0(as.character(plot_label, plot_label2, sep = " ")),
# size=4)
In_pH_Modeled
Mid channel
hist(Trends.midchanel$pH_calculated)
shapiro.test(Trends.midchanel$pH_calculated)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.midchanel$pH_calculated
## W = 0.97575, p-value = 3.242e-05pH_mc_0<-lmer(pH_calculated~Year * Sub_region + (1|Month) +
#(1|Zone)+
(1|SiteID),
data=Trends.midchanel)
#summary(pH_mc_0)
anova(pH_mc_0) ranova(pH_mc_0) step(pH_mc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 575.00 -1128.00
## (1 | SiteID) 1 10 574.49 -1128.99 1.017 1 0.3133
## (1 | Month) 0 9 461.28 -904.56 226.423 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 0.0031140 0.0010380 3 299.99 0.9248 0.4291
## Year 2 0.0002402 0.0002402 1 312.24 0.2142 0.6438
## Sub_region 0 0.0314885 0.0104962 3 304.03 9.3869 5.957e-06
##
## Year:Sub_region
## Year
## Sub_region ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## pH_calculated ~ Sub_region + (1 | Month)pH_mc<-lmer(pH_calculated ~ Year + Sub_region + (1 | Month),
data = Trends.midchanel)
summary(pH_mc)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pH_calculated ~ Year + Sub_region + (1 | Month)
## Data: Trends.midchanel
##
## REML criterion at convergence: -1177.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3996 -0.6123 0.0126 0.5291 4.1156
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.001746 0.04178
## Residual 0.001122 0.03349
## Number of obs: 319, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.960e+00 2.328e+00 3.122e+02 2.990 0.00302 **
## Year 5.339e-04 1.154e-03 3.122e+02 0.463 0.64384
## Sub_regionUK 1.668e-02 6.062e-03 3.030e+02 2.752 0.00628 **
## Sub_regionMK 6.264e-03 5.376e-03 3.030e+02 1.165 0.24491
## Sub_regionLK -1.117e-02 5.367e-03 3.031e+02 -2.081 0.03823 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.020 0.019
## Sub_reginMK -0.004 0.003 0.530
## Sub_reginLK -0.026 0.025 0.533 0.602 anova(pH_mc) plot(pH_mc)
qqnorm(resid(pH_mc))
acf(resid(pH_mc), main="acf(resid(pH Mid Channel))") 
pH_mc.emmc<-emmeans(pH_mc, ~Sub_region)
pH_mc.emmc<-multcomp::cld(pH_mc.emmc)
pH_mc.emmc<-pH_mc.emmc[order(pH_mc.emmc$Sub_region),]
pH_mc.emmc #write.csv(pH_mc.emmc, "FLK_results/Model_6_LTpH_POST.csv")# Predict values
Mc_pH_Pred <- predict(pH_mc, newdata=Mc_Newdata,
re.form=NA, level=0, asList = FALSE)
Mc_pH_Pred <-cbind(Mc_Newdata, Mc_pH_Pred)
Mc_pH_Pred$Date<-as.Date(as.character(Mc_pH_Pred$Year), format = "%Y")
# Model summary for slope labels
slope<-round(
summary(pH_mc)$coefficients["Year", "Estimate"], digits = 3)
#xs <- as.Date(as.character("2018-11-20"))
#ys <- max(Trends.inshore$pH_calculated, na.rm =T)
ys <- max(Trends.midchanel$pH_calculated, na.rm =T)+0.03
plot_label <- sprintf("\"\" ~ pH == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Mc_pH_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(name=(expression(pH[calculated])),
breaks = seq(0, 10, 0.1),
limits=c(7.80, 8.28),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.midchanel,
aes(y=pH_calculated, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=23)+
geom_line(data=Mc_pH_Pred, aes(y = Mc_pH_Pred, x=Date, colour = Sub_region),
linetype=1, size=1) +
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Mc_pH_Modeled
Offshore
hist(Trends.offshore$pH_calculated)
shapiro.test(Trends.offshore$pH_calculated)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.offshore$pH_calculated
## W = 0.99092, p-value = 0.01729pH_of_0<-lmer(pH_calculated~Year * Sub_region +
(1|Month) + (1|SiteID),
data=Trends.offshore)
#summary(pH_of_0)
anova(pH_of_0) ranova(pH_of_0) step(pH_of_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 920.71 -1819.4
## (1 | SiteID) 1 10 920.71 -1821.4 0.00 1 1
## (1 | Month) 0 9 767.35 -1516.7 306.73 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 0 0.0033325 0.0011108 3 368.84 3.037 0.02913 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## pH_calculated ~ Year + Sub_region + (1 | Month) + Year:Sub_regionpH_of<-lmer(pH_calculated ~ Year + Sub_region + (1 | Month),
data =Trends.offshore)
summary(pH_of) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pH_calculated ~ Year + Sub_region + (1 | Month)
## Data: Trends.offshore
##
## REML criterion at convergence: -1867.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5593 -0.5678 0.0880 0.6617 3.0799
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.0007452 0.02730
## Residual 0.0003717 0.01928
## Number of obs: 388, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.043e+01 1.215e+00 3.800e+02 8.591 2.26e-16 ***
## Year -1.188e-03 6.019e-04 3.799e+02 -1.973 0.0492 *
## Sub_regionUK 3.608e-03 3.106e-03 3.719e+02 1.162 0.2461
## Sub_regionMK 5.338e-03 3.072e-03 3.719e+02 1.738 0.0831 .
## Sub_regionLK -3.117e-03 2.923e-03 3.719e+02 -1.066 0.2869
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.008 0.006
## Sub_reginMK -0.007 0.006 0.590
## Sub_reginLK -0.021 0.019 0.622 0.630 anova(pH_of) plot(pH_of)
qqnorm(resid(pH_of))
acf(resid(pH_of), main="acf(resid(pH Offshore))")
pH_of.emmc<-emmeans(pH_of, ~Sub_region)
pH_of.emmc<-multcomp::cld(pH_of.emmc)
pH_of.emmc<-pH_of.emmc[order(pH_of.emmc$Sub_region),]
pH_of.emmc #write.csv(pH_of.emmc, "FLK_results/Model_7_LTpH_POST.csv")# Predict values
Of_pH_Pred <- predict(pH_of, newdata=Of_Newdata,
re.form=NA, level=0, asList = FALSE)
Of_pH_Pred <-cbind(Of_Newdata, Of_pH_Pred)
Of_pH_Pred$Date<-as.Date(as.character(Of_pH_Pred$Year), format = "%Y")
# Model summary for slope labels
slope<-round(
summary(pH_of)$coefficients["Year", "Estimate"], digits = 3)
#ys <- max(Trends.inshore$pH_calculated, na.rm =T)
ys <- max(Trends.offshore$pH_calculated, na.rm =T)+0.08
plot_label <- sprintf("\"\" ~ pH == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Of_pH_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(name=(expression(pH[calculated])),
limits=c(7.88, 8.2),
# limits=c(7.80, 8.28),
breaks = seq(0, 10, 0.1),
expand = c(0, 0.01)) +
geom_point(data=Trends.offshore,
aes(y=pH_calculated, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=24)+
geom_line(data=Of_pH_Pred, aes(y = Of_pH_Pred, x=Date, colour = Sub_region),
linetype=1, size=1) +
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Of_pH_Modeled
Oceanic
hist(Trends.ocean$pH_calculated)
shapiro.test(Trends.ocean$pH_calculated)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.ocean$pH_calculated
## W = 0.93998, p-value = 6.014e-06pH_oc_0<-lmer(pH_calculated ~ Year * Sub_region +
(1 | Month)+ (1|SiteID),
data=Trends.ocean)
#summary(pH_oc_0)
anova(pH_oc_0) ranova(pH_oc_0) step(pH_oc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 9 324.53 -631.05
## (1 | SiteID) 1 8 324.53 -633.05 0.000 1 0.9999
## (1 | Month) 0 7 305.14 -596.27 38.776 1 4.753e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 0.00141777 0.00070889 2 132.05 1.6468 0.1966
## Sub_region 2 0.00073784 0.00036892 2 134.10 0.8493 0.4300
## Year 3 0.00087168 0.00087168 1 144.81 2.0100 0.1584
##
## Model found:
## pH_calculated ~ (1 | Month)pH_oc<-lmer(pH_calculated ~ Year + (1 | Month),
data=Trends.ocean)
summary(pH_oc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pH_calculated ~ Year + (1 | Month)
## Data: Trends.ocean
##
## REML criterion at convergence: -683.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.5077 -0.4574 0.0159 0.5652 2.1918
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.0002218 0.01489
## Residual 0.0004337 0.02082
## Number of obs: 148, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.087e+01 1.996e+00 1.448e+02 5.445 2.17e-07 ***
## Year -1.403e-03 9.892e-04 1.448e+02 -1.418 0.158
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Year -1.000 anova(pH_oc) plot(pH_oc)
qqnorm(resid(pH_oc))
acf(resid(pH_oc), main="acf(resid(pH ocean))")
pH_oc.emmc<-emmeans(pH_oc, ~Year)
pH_oc.emmc<-multcomp::cld(pH_oc.emmc)
#pH_oc.emmc<-pH_oc.emmc[order(pH_oc.emmc$Sub_region),]
pH_oc.emmc# Predict values
Oc_pH_Pred <- predict(pH_oc, newdata=Oc_Newdata,
re.form=NA, level=0, asList = FALSE)
Oc_pH_Pred <-cbind(Oc_Newdata, Oc_pH_Pred)
Oc_pH_Pred$Date<-as.Date(as.character(Oc_pH_Pred$Year), format = "%Y")
# Model summary for slope labels
slope<-round(
summary(pH_oc)$coefficients["Year", "Estimate"], digits = 3)
#ys <- max(Trends.inshore$pH_calculated, na.rm =T)
ys <- max(Trends.offshore$pH_calculated, na.rm =T)+0.08
plot_label <- sprintf("\"\" ~ pH == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Oc_pH_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(name=(expression(pH[calculated])),
limits=c(7.88, 8.2),
# limits=c(7.80, 8.28),
breaks = seq(0, 10, 0.1),
expand = c(0, 0.01)) +
geom_point(data=Trends.ocean,
aes(y=pH_calculated, x=Date, #fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=25)+
geom_line(data=Oc_pH_Pred, aes(y = Oc_pH_Pred, x=Date),
linetype=1, size=1, colour = "black") +
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Oc_pH_Modeled
Models summary
Complete models
# ANOVAs
# Convert ANOVA results into dataframes allows for easier name manipulation
av.pH_in_0 <- data.frame (anova(pH_in_0))
av.pH_mc_0 <- data.frame (anova(pH_mc_0) )
av.pH_of_0 <- data.frame (anova(pH_of_0))
av.pH_oc_0 <- data.frame (anova(pH_oc_0))
# Putting all into one dataframe/table
anova_pH <- data.frame(cbind
(c("Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region"),
rbind(av.pH_in_0, av.pH_mc_0,
av.pH_of_0, av.pH_oc_0)))
colnames(anova_pH) <- c("", "Sum Sq", "Mean Sq", "N Df" , "D Df",
"F value", "Pr(>F)")
row.names(anova_pH) <- NULL
# create HTML table using kableExtra
anova_pH %>% kable("html", digits=2) %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "pH inshore", 1, 3) %>% # groups rows with label
pack_rows(., "pH mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "pH offshore", 7, 9) %>% # groups rows with label
pack_rows(., "pH ocean", 10, 12) | Sum Sq | Mean Sq | N Df | D Df | F value | Pr(>F) | |
|---|---|---|---|---|---|---|
| pH inshore | ||||||
| Year | 0.00 | 0 | 1 | 329.42 | 0.14 | 0.71 |
| Sub_region | 0.01 | 0 | 3 | 321.24 | 0.80 | 0.49 |
| Year:Sub_region | 0.01 | 0 | 3 | 321.24 | 0.80 | 0.49 |
| pH mid-channel | ||||||
| Year | 0.00 | 0 | 1 | 303.84 | 0.12 | 0.73 |
| Sub_region | 0.00 | 0 | 3 | 294.68 | 0.95 | 0.42 |
| Year:Sub_region | 0.00 | 0 | 3 | 294.68 | 0.95 | 0.42 |
| pH offshore | ||||||
| Year | 0.00 | 0 | 1 | 376.66 | 5.67 | 0.02 |
| Sub_region | 0.00 | 0 | 3 | 368.90 | 3.04 | 0.03 |
| Year:Sub_region | 0.00 | 0 | 3 | 368.90 | 3.04 | 0.03 |
| pH ocean | ||||||
| Year | 0.00 | 0 | 1 | 141.59 | 0.90 | 0.34 |
| Sub_region | 0.00 | 0 | 2 | 132.05 | 1.65 | 0.20 |
| Year:Sub_region | 0.00 | 0 | 2 | 132.05 | 1.65 | 0.20 |
# R-ANOVAs
rav.pH_in_0 <- data.frame (ranova(pH_in_0))
rav.pH_mc_0 <- data.frame (ranova(pH_mc_0) )
rav.pH_of_0 <- data.frame (ranova(pH_of_0))
rav.pH_oc_0 <- data.frame (ranova(pH_oc_0))
# Putting all into one dataframe/table
ranova_pH <- data.frame(cbind
(c("<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)"),
rbind(rav.pH_in_0, rav.pH_mc_0,
rav.pH_of_0, rav.pH_oc_0)))
colnames(ranova_pH) <- c("", "npar", "logLik", "AIC" , "LRT",
"DF", "Pr(>Chi Sq)")
row.names(ranova_pH) <- NULL
# create HTML table using kableExtra
ranova_pH %>% kable("html", digits=2) %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "pH inshore", 1, 3) %>% # groups rows with label
pack_rows(., "pH mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "pH offshore", 7, 9) %>% # groups rows with label
pack_rows(., "pH ocean", 10, 12) | npar | logLik | AIC | LRT | DF | Pr(>Chi Sq) | |
|---|---|---|---|---|---|---|
| pH inshore | ||||||
| <none> | 11 | 464.47 | -906.94 | |||
| (1 | Month) | 10 | 316.60 | -613.19 | 295.75 | 1 | 0.00 |
| (1 | SiteID) | 10 | 461.43 | -902.87 | 6.07 | 1 | 0.01 |
| pH mid-channel | ||||||
| <none> | 11 | 575.00 | -1128.00 | |||
| (1 | Month) | 10 | 461.28 | -902.56 | 227.44 | 1 | 0.00 |
| (1 | SiteID) | 10 | 574.49 | -1128.99 | 1.02 | 1 | 0.31 |
| pH offshore | ||||||
| <none> | 11 | 920.71 | -1819.43 | |||
| (1 | Month) | 10 | 767.35 | -1514.69 | 306.73 | 1 | 0.00 |
| (1 | SiteID) | 10 | 920.71 | -1821.43 | 0.00 | 1 | 1.00 |
| pH ocean | ||||||
| <none> | 9 | 324.53 | -631.05 | |||
| (1 | Month) | 8 | 305.14 | -594.27 | 38.78 | 1 | 0.00 |
| (1 | SiteID) | 8 | 324.53 | -633.05 | 0.00 | 1 | 1.00 |
pH_models_0<-list("Inshore" = pH_in_0,
"Mid channel" = pH_mc_0,
"Offshore" = pH_of_0,
"Ocean" = pH_oc_0)
modelsummary(pH_models_0, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'pH model outputs (all factors)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | −0.002 | −0.001 | −0.003* | 0.000 |
| (0.003) | (0.002) | (0.001) | (0.002) | |
| Intercept | 12.081+ | 10.029* | 13.706*** | 7.416+ |
| (6.677) | (4.209) | (2.397) | (3.975) | |
| Upper Keys | −4.133 | −2.200 | −3.793 | |
| (8.451) | (5.768) | (2.962) | ||
| Middle Keys | −5.187 | −1.337 | −0.425 | 6.694 |
| (8.190) | (5.104) | (2.934) | (4.539) | |
| Lower Keys | −12.284 | −7.655 | −6.779* | 1.147 |
| (8.252) | (5.153) | (2.796) | (4.606) | |
| Num.Obs. | 347 | 319 | 388 | 148 |
| R2 Marg. | 0.046 | 0.037 | 0.024 | 0.052 |
| R2 Cond. | 0.686 | 0.629 | 0.678 | |
| AIC | −906.9 | −1128.0 | −1819.4 | −631.1 |
| BIC | −864.6 | −1086.6 | −1775.9 | −604.1 |
| ICC | 0.7 | 0.6 | 0.7 | |
| RMSE | 0.05 | 0.03 | 0.02 | 0.02 |
| + p |
#modelsummary(pH_models, estimate = "p.value")Factors removed models
pH_models<-list("Inshore" = pH_in,
"Mid channel" = pH_mc,
"Offshore" = pH_of,
"Ocean" = pH_oc)
modelsummary(pH_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'pH model outputs (factors dropped)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | 0.001 | −0.001* | −0.001 | |
| (0.001) | (0.001) | (0.001) | ||
| Intercept | 8.029*** | 6.960** | 10.434*** | 10.868*** |
| (0.025) | (2.328) | (1.215) | (1.996) | |
| Upper Keys | 0.022 | 0.017** | 0.004 | |
| (0.015) | (0.006) | (0.003) | ||
| Middle Keys | 0.018 | 0.006 | 0.005+ | |
| (0.015) | (0.005) | (0.003) | ||
| Lower Keys | −0.027+ | −0.011* | −0.003 | |
| (0.015) | (0.005) | (0.003) | ||
| Num.Obs. | 347 | 319 | 388 | 148 |
| R2 Marg. | 0.043 | 0.034 | 0.016 | 0.013 |
| R2 Cond. | 0.689 | 0.622 | 0.673 | 0.347 |
| AIC | −951.5 | −1163.1 | −1853.2 | −675.7 |
| BIC | −924.6 | −1136.8 | −1825.5 | −663.7 |
| ICC | 0.7 | 0.6 | 0.7 | 0.3 |
| RMSE | 0.05 | 0.03 | 0.02 | 0.02 |
| + p |
#modelsummary(pH_models, estimate = "p.value")Trends_pH<-ggarrange(In_pH_Modeled+ theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
legend.position = "none"),
Mc_pH_Modeled + theme(legend.position = "none",
axis.text.x=element_blank(),
axis.title.y=element_blank()),
Of_pH_Modeled + theme(legend.position = "none",
axis.text.x=element_blank(),
axis.title.y=element_blank()),
Oc_pH_Modeled + theme(legend.position = "none",
axis.text.x=element_blank(),
axis.title.y=element_blank()),
labels = c("B"),
ncol = 4, nrow = 1, widths =c(1.1, 1, 1, 1))
Trends_pH
TA-All
##
## Shapiro-Wilk normality test
##
## data: Trends.data$TA_umol_kg
## W = 0.8049, p-value < 2.2e-16
Inshore
# Inshore TA
hist(Trends.inshore$TA_umol_kg)
shapiro.test(Trends.inshore$TA_umol_kg)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.inshore$TA_umol_kg
## W = 0.97145, p-value = 2.182e-06TA_in_0<-lmer(TA_umol_kg~Year * Sub_region + (1|Month)+
#(1|Zone)+
(1|SiteID), data=Trends.inshore)
#summary(TA_in_0)
anova(TA_in_0) ranova(TA_in_0) step(TA_in_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1990.5 4003.0
## (1 | SiteID) 1 10 -1991.5 4002.9 1.923 1 0.1656
## (1 | Month) 0 9 -2069.8 4157.7 156.779 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 26521 8840 3 331.02 1.6535 0.1769
## Year 2 26 26 1 344.93 0.0049 0.9441
## Sub_region 0 560636 186879 3 335.08 34.8434 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## TA_umol_kg ~ Sub_region + (1 | Month)TA_in<-lmer(TA_umol_kg ~ Sub_region + (1 | Month),
data=Trends.inshore)
summary(TA_in) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TA_umol_kg ~ Sub_region + (1 | Month)
## Data: Trends.inshore
##
## REML criterion at convergence: 4006.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5370 -0.5310 0.0517 0.5682 4.7726
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 4637 68.10
## Residual 5363 73.24
## Number of obs: 350, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2375.078 21.652 14.670 109.693 < 2e-16 ***
## Sub_regionUK 3.191 11.896 334.979 0.268 0.78870
## Sub_regionMK -32.099 11.701 334.967 -2.743 0.00641 **
## Sub_regionLK -94.998 11.622 335.135 -8.174 6.21e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sb_rUK Sb_rMK
## Sub_reginUK -0.312
## Sub_reginMK -0.318 0.576
## Sub_reginLK -0.321 0.578 0.593 plot(TA_in)
qqnorm(resid(TA_in))
acf(resid(TA_in), main="acf(resid(TA Inshore))") 
TA_in.emmc<-emmeans(TA_in, ~Sub_region)
TA_in.emmc<-multcomp::cld(TA_in.emmc)
TA_in.emmc<-TA_in.emmc[order(TA_in.emmc$Sub_region),]
TA_in.emmcMid channel
hist(Trends.midchanel$TA_umol_kg)
shapiro.test(Trends.midchanel$TA_umol_kg)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.midchanel$TA_umol_kg
## W = 0.87228, p-value = 1.176e-15TA_mc_0<-lmer(TA_umol_kg~Year * Sub_region + (1|Month) +
#(1|Zone)+
(1|SiteID),
data=Trends.midchanel)
#summary(TA_mc_0)
anova(TA_mc_0) ranova(TA_mc_0) step(TA_mc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1664.4 3350.8
## (1 | Month) 0 10 -1702.0 3424.0 75.168 1 < 2e-16 ***
## (1 | SiteID) 0 10 -1665.8 3351.7 2.878 1 0.08979 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 6526.0 2175.3 3 296.129 1.1327 0.33606
## Year 2 605.8 605.8 1 305.751 0.3149 0.57510
## Sub_region 0 27707.5 9235.8 3 6.215 4.8026 0.04686 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## TA_umol_kg ~ Sub_region + (1 | Month) + (1 | SiteID)TA_mc<-lmer(TA_umol_kg ~ (1 | Month) + (1|SiteID),
data = Trends.midchanel)
summary(TA_mc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TA_umol_kg ~ (1 | Month) + (1 | SiteID)
## Data: Trends.midchanel
##
## REML criterion at convergence: 3376.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.1017 -0.4452 0.1164 0.5311 3.4719
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 905.6 30.09
## SiteID (Intercept) 251.2 15.85
## Residual 1923.6 43.86
## Number of obs: 321, groups: Month, 12; SiteID, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2362.52 10.36 15.75 228.1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 plot(TA_mc)
qqnorm(resid(TA_mc))
acf(resid(TA_mc), main="acf(resid(TA Mid Channel))") 
Offshore
hist(Trends.offshore$TA_umol_kg)
shapiro.test(Trends.offshore$TA_umol_kg)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.offshore$TA_umol_kg
## W = 0.88004, p-value < 2.2e-16TA_of_0<-lmer(TA_umol_kg~Year * Sub_region +
(1|Month) + (1|SiteID),
data=Trends.offshore)
#summary(TA_of_0)
anova(TA_of_0) ranova(TA_of_0) step(TA_of_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1681.9 3385.8
## (1 | SiteID) 1 10 -1681.9 3383.8 0.000 1 1
## (1 | Month) 0 9 -1712.8 3443.7 61.928 1 3.562e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 193.64 64.55 3 371.60 0.2024 0.89468
## Year 2 3.81 3.81 1 366.89 0.0120 0.91276
## Sub_region 0 2944.96 981.65 3 376.33 3.1051 0.02658 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## TA_umol_kg ~ Sub_region + (1 | Month)TA_of<-lmer(TA_umol_kg ~ Year + Sub_region + (1 | Month),
data =Trends.offshore)
summary(TA_of) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TA_umol_kg ~ Year + Sub_region + (1 | Month)
## Data: Trends.offshore
##
## REML criterion at convergence: 3370.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.6937 -0.3550 0.0732 0.5895 3.5239
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 111.4 10.56
## Residual 316.8 17.80
## Number of obs: 391, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2498.26924 1087.51333 366.86056 2.297 0.0222 *
## Year -0.05908 0.53893 366.88694 -0.110 0.9128
## Sub_regionUK 0.27805 2.86697 374.57369 0.097 0.9228
## Sub_regionMK -6.12303 2.83594 374.56360 -2.159 0.0315 *
## Sub_regionLK -4.77981 2.69007 375.14829 -1.777 0.0764 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.007 0.006
## Sub_reginMK -0.007 0.006 0.590
## Sub_reginLK -0.014 0.012 0.624 0.632 plot(TA_of)
qqnorm(resid(TA_of))
acf(resid(TA_of), main="acf(resid(TA Offshore))") 
TA_of.emmc<-emmeans(TA_of, ~Sub_region)
TA_of.emmc<-multcomp::cld(TA_of.emmc)
TA_of.emmc<-TA_of.emmc[order(TA_of.emmc$Sub_region),]
TA_of.emmcOceanic
hist(Trends.ocean$TA_umol_kg)
shapiro.test(Trends.ocean$TA_umol_kg)# Normality NOT rejected##
## Shapiro-Wilk normality test
##
## data: Trends.ocean$TA_umol_kg
## W = 0.98837, p-value = 0.2463TA_oc_0<-lmer(TA_umol_kg ~ Year * Sub_region +
(1 | Month)+ (1|SiteID),
data=Trends.ocean)
#summary(TA_oc_0)
anova(TA_oc_0) ranova(TA_oc_0) step(TA_oc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 9 -557.54 1133.1
## (1 | SiteID) 1 8 -557.54 1131.1 0.000 1 1.0000000
## (1 | Month) 0 7 -564.58 1143.2 14.069 1 0.0001762 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 65.27 32.63 2 133.56 0.3261 0.72234
## Sub_region 2 323.50 161.75 2 135.60 1.6308 0.19958
## Year 0 409.94 409.94 1 134.74 4.0813 0.04534 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## TA_umol_kg ~ Year + (1 | Month)TA_oc<-lmer(TA_umol_kg ~ (1 | Month),
data = Trends.ocean)
summary(TA_oc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TA_umol_kg ~ (1 | Month)
## Data: Trends.ocean
##
## REML criterion at convergence: 1133
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.06664 -0.52823 0.09589 0.57325 2.74375
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 30.58 5.53
## Residual 101.45 10.07
## Number of obs: 150, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2380.456 1.812 10.623 1313 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 plot(TA_oc)
qqnorm(resid(TA_oc))
acf(resid(TA_oc), main="acf(resid(TA_Ocean))")
# TA_oc.emmc<-emmeans(TA_oc, ~Sub_region)
# TA_oc.emmc<-multcomp::cld(TA_oc.emmc)
# TA_oc.emmc<-TA_oc.emmc[order(TA_oc.emmc$Sub_region),]
# TA_oc.emmcModels summary
Complete models
# ANOVAs
# Convert ANOVA results into dataframes allows for easier name manipulation
av.TA_in_0 <- data.frame (anova(TA_in_0))
av.TA_mc_0 <- data.frame (anova(TA_mc_0) )
av.TA_of_0 <- data.frame (anova(TA_of_0))
av.TA_oc_0 <- data.frame (anova(TA_oc_0))
# Putting all into one dataframe/table
anova_TA_0 <- data.frame(cbind
(c("Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region"),
rbind(av.TA_in_0, av.TA_mc_0,
av.TA_of_0, av.TA_oc_0)))
colnames(anova_TA_0) <- c("", "Sum Sq", "Mean Sq", "N Df" , "D Df",
"F value", "Pr(>F)")
row.names(anova_TA_0) <- NULL
# create HTML table using kableExtra
anova_TA_0 %>% kable("html", digits=2) %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "TA inshore", 1, 3) %>% # groups rows with label
pack_rows(., "TA mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "TA offshore", 7, 9) %>% # groups rows with label
pack_rows(., "TA ocean", 10, 12) | Sum Sq | Mean Sq | N Df | D Df | F value | Pr(>F) | |
|---|---|---|---|---|---|---|
| TA inshore | ||||||
| Year | 244.12 | 244.12 | 1 | 335.96 | 0.05 | 0.83 |
| Sub_region | 26861.54 | 8953.85 | 3 | 324.67 | 1.71 | 0.17 |
| Year:Sub_region | 26923.58 | 8974.53 | 3 | 324.67 | 1.71 | 0.16 |
| TA mid-channel | ||||||
| Year | 1043.76 | 1043.76 | 1 | 303.96 | 0.54 | 0.46 |
| Sub_region | 6526.36 | 2175.45 | 3 | 296.13 | 1.13 | 0.34 |
| Year:Sub_region | 6525.95 | 2175.32 | 3 | 296.13 | 1.13 | 0.34 |
| TA offshore | ||||||
| Year | 15.13 | 15.13 | 1 | 366.42 | 0.05 | 0.83 |
| Sub_region | 194.42 | 64.81 | 3 | 371.64 | 0.20 | 0.89 |
| Year:Sub_region | 193.64 | 64.55 | 3 | 371.64 | 0.20 | 0.89 |
| TA ocean | ||||||
| Year | 406.47 | 406.47 | 1 | 136.76 | 4.06 | 0.05 |
| Sub_region | 64.99 | 32.49 | 2 | 133.54 | 0.32 | 0.72 |
| Year:Sub_region | 65.27 | 32.63 | 2 | 133.54 | 0.33 | 0.72 |
# R-ANOVAs
rav.TA_in_0 <- data.frame (ranova(TA_in_0))
rav.TA_mc_0 <- data.frame (ranova(TA_mc_0) )
rav.TA_of_0 <- data.frame (ranova(TA_of_0))
rav.TA_oc_0 <- data.frame (ranova(TA_oc_0))
# Putting all into one dataframe/table
ranova_TA_0 <- data.frame(cbind
(c("<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)"),
rbind(rav.TA_in_0, rav.TA_mc_0,
rav.TA_of_0, rav.TA_oc_0)))
colnames(ranova_TA_0) <- c("", "npar", "logLik", "AIC" , "LRT",
"DF", "Pr(>Chi Sq)")
row.names(ranova_TA_0) <- NULL
# create HTML table using kableExtra
ranova_TA_0 %>% kable("html", digits=2) %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "TA inshore", 1, 3) %>% # groups rows with label
pack_rows(., "TA mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "TA offshore", 7, 9) %>% # groups rows with label
pack_rows(., "TA ocean", 10, 12)| npar | logLik | AIC | LRT | DF | Pr(>Chi Sq) | |
|---|---|---|---|---|---|---|
| TA inshore | ||||||
| <none> | 11 | -1990.48 | 4002.97 | |||
| (1 | Month) | 10 | -2069.77 | 4159.55 | 158.58 | 1 | 0.00 |
| (1 | SiteID) | 10 | -1991.45 | 4002.89 | 1.92 | 1 | 0.17 |
| TA mid-channel | ||||||
| <none> | 11 | -1664.40 | 3350.80 | |||
| (1 | Month) | 10 | -1701.99 | 3423.97 | 75.17 | 1 | 0.00 |
| (1 | SiteID) | 10 | -1665.84 | 3351.68 | 2.88 | 1 | 0.09 |
| TA offshore | ||||||
| <none> | 11 | -1681.89 | 3385.78 | |||
| (1 | Month) | 10 | -1712.85 | 3445.71 | 61.93 | 1 | 0.00 |
| (1 | SiteID) | 10 | -1681.89 | 3383.78 | 0.00 | 1 | 1.00 |
| TA ocean | ||||||
| <none> | 9 | -557.54 | 1133.09 | |||
| (1 | Month) | 8 | -564.58 | 1145.16 | 14.07 | 1 | 0.00 |
| (1 | SiteID) | 8 | -557.54 | 1131.09 | 0.00 | 1 | 1.00 |
TA_models_0<-list("Inshore" = TA_in_0,
"Mid channel" = TA_mc_0,
"Offshore" = TA_of_0,
"Ocean" = TA_oc_0)
modelsummary(TA_models_0, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'TA model outputs (all factors)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | −2.080 | −1.670 | −0.782 | 1.296 |
| (4.457) | (2.732) | (1.101) | (0.942) | |
| Intercept | 6572.526 | 5746.490 | 3957.018+ | −234.002 |
| (8992.919) | (5512.076) | (2222.111) | (1899.673) | |
| Upper Keys | −3.961 | 1753.091 | −1817.493 | |
| (11409.409) | (7598.946) | (2764.945) | ||
| Middle Keys | −16949.096 | −8097.847 | −1984.851 | 402.836 |
| (11058.674) | (6722.297) | (2738.961) | (2186.370) | |
| Lower Keys | 4226.694 | 1762.679 | −1551.341 | 1534.929 |
| (11069.985) | (6758.221) | (2600.459) | (2211.616) | |
| Num.Obs. | 350 | 321 | 391 | 150 |
| R2 Marg. | 0.147 | 0.070 | 0.025 | 0.060 |
| R2 Cond. | 0.556 | 0.398 | ||
| AIC | 4003.0 | 3350.8 | 3385.8 | 1133.1 |
| BIC | 4045.4 | 3392.3 | 3429.4 | 1160.2 |
| ICC | 0.5 | 0.4 | ||
| RMSE | 70.11 | 42.33 | 17.44 | 9.52 |
| + p |
#modelsummary(TA_models, estimate = "p.value")Factors removed models
TA_models<-list("Inshore" = TA_in,
"Mid channel" = TA_mc,
"Offshore" = TA_of,
"Ocean" = TA_oc)
modelsummary(TA_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'TA model outputs (dropped facators)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | −0.059 | |||
| (0.539) | ||||
| Intercept | 2375.078*** | 2362.521*** | 2498.269* | 2380.456*** |
| (21.652) | (10.357) | (1087.513) | (1.812) | |
| Upper Keys | 3.191 | 0.278 | ||
| (11.896) | (2.867) | |||
| Middle Keys | −32.099** | −6.123* | ||
| (11.701) | (2.836) | |||
| Lower Keys | −94.998*** | −4.780+ | ||
| (11.622) | (2.690) | |||
| Num.Obs. | 350 | 321 | 391 | 150 |
| R2 Marg. | 0.141 | 0.000 | 0.018 | 0.000 |
| R2 Cond. | 0.539 | 0.376 | 0.273 | 0.232 |
| AIC | 4018.4 | 3384.7 | 3384.5 | 1139.0 |
| BIC | 4041.5 | 3399.8 | 3412.3 | 1148.0 |
| ICC | 0.5 | 0.4 | 0.3 | 0.2 |
| RMSE | 71.70 | 42.59 | 17.46 | 9.75 |
| + p |
#modelsummary(TA_models, estimate = "p.value")DIC-All
##
## Shapiro-Wilk normality test
##
## data: Trends.data$DIC_umol_kg
## W = 0.88724, p-value < 2.2e-16
Inshore
hist(Trends.inshore$DIC_umol_kg)
shapiro.test(Trends.inshore$DIC_umol_kg)# Normality NOT rejected##
## Shapiro-Wilk normality test
##
## data: Trends.inshore$DIC_umol_kg
## W = 0.99654, p-value = 0.66# Inshore DIC
DIC_in<-lmer(DIC_umol_kg~Year * Sub_region + (1|Month)+
#(1|Zone)+
(1|SiteID),
data=Trends.inshore)
#summary(DIC_in)
anova(DIC_in) ranova(DIC_in) step(DIC_in)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1975.9 3973.8
## (1 | Month) 0 10 -2094.1 4208.2 236.472 1 < 2e-16 ***
## (1 | SiteID) 0 10 -1978.0 3976.0 4.218 1 0.03999 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 25796 8598.5 3 321.52 1.6460 0.17865
## Year 2 67 66.8 1 334.73 0.0127 0.91027
## Sub_region 0 94664 31554.6 3 7.15 6.0221 0.02285 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## DIC_umol_kg ~ Sub_region + (1 | Month) + (1 | SiteID)DIC_in<-lmer(DIC_umol_kg ~ Year + Sub_region +
(1|Month) + (1|SiteID), data=Trends.inshore)
summary(DIC_in) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DIC_umol_kg ~ Year + Sub_region + (1 | Month) + (1 | SiteID)
## Data: Trends.inshore
##
## REML criterion at convergence: 3971.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1985 -0.6311 -0.0178 0.5971 4.1226
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 7095.6 84.24
## SiteID (Intercept) 251.2 15.85
## Residual 5255.8 72.50
## Number of obs: 347, groups: Month, 12; SiteID, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1505.8479 4868.1758 334.7412 0.309 0.7573
## Year 0.2721 2.4124 334.7290 0.113 0.9103
## Sub_regionUK -16.5526 18.6644 7.1543 -0.887 0.4040
## Sub_regionMK -45.7023 18.5361 6.9628 -2.466 0.0433 *
## Sub_regionLK -70.2130 18.5236 6.9438 -3.790 0.0069 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.027 0.025
## Sub_reginMK -0.014 0.011 0.591
## Sub_reginLK -0.026 0.024 0.590 0.596 plot(DIC_in)
qqnorm(resid(DIC_in))
acf(resid(DIC_in), main="acf(resid(DIC Inshore))")
DIC_in.emmc<-emmeans(DIC_in, ~Sub_region+Year)
DIC_in.emmc<-multcomp::cld(DIC_in.emmc)
DIC_in.emmc<-DIC_in.emmc[order(DIC_in.emmc$Sub_region),]
DIC_in.emmcMid channel
hist(Trends.midchanel$DIC_umol_kg)
shapiro.test(Trends.midchanel$DIC_umol_kg)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.midchanel$DIC_umol_kg
## W = 0.93309, p-value = 8.64e-11DIC_mc<-lmer(DIC_umol_kg~Year * Sub_region + (1|Month) +
#(1|Zone)+
(1|SiteID), data=Trends.midchanel)
#summary(DIC_mc)
anova(DIC_mc) ranova(DIC_mc) step(DIC_mc)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1662.8 3347.5
## (1 | SiteID) 1 10 -1663.5 3346.9 1.402 1 0.2363
## (1 | Month) 0 9 -1728.8 3475.7 130.735 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 8960.2 2986.7 3 299.85 1.4641 0.224389
## Year 2 2124.8 2124.8 1 313.93 1.0364 0.309433
## Sub_region 0 29902.5 9967.5 3 304.07 4.8510 0.002598 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## DIC_umol_kg ~ Sub_region + (1 | Month)DIC_mc<-lmer(DIC_umol_kg ~ Year + Sub_region + (1 | Month),
data = Trends.midchanel)
summary(DIC_mc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DIC_umol_kg ~ Year + Sub_region + (1 | Month)
## Data: Trends.midchanel
##
## REML criterion at convergence: 3343.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3732 -0.4413 0.0604 0.5350 3.9154
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 1700 41.23
## Residual 2050 45.28
## Number of obs: 319, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5223.535 3120.132 313.931 1.674 0.095099 .
## Year -1.574 1.546 313.932 -1.018 0.309433
## Sub_regionUK -8.601 8.196 302.877 -1.049 0.294825
## Sub_regionMK -21.738 7.268 302.850 -2.991 0.003011 **
## Sub_regionLK -24.593 7.256 302.976 -3.389 0.000793 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.020 0.018
## Sub_reginMK -0.004 0.003 0.530
## Sub_reginLK -0.026 0.024 0.533 0.602 plot(DIC_mc)
qqnorm(resid(DIC_mc))
acf(resid(DIC_mc), main="acf(resid(DIC Mid Channel))") 
DIC_mc.emmc<-emmeans(DIC_mc, ~Sub_region+Year)
DIC_mc.emmc<-multcomp::cld(DIC_mc.emmc)
DIC_mc.emmc<-DIC_mc.emmc[order(DIC_mc.emmc$Sub_region),]
DIC_mc.emmcOffshore
hist(Trends.offshore$DIC_umol_kg)
shapiro.test(Trends.offshore$DIC_umol_kg)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.offshore$DIC_umol_kg
## W = 0.92737, p-value = 8.011e-13DIC_of<-lmer(DIC_umol_kg~Year * Sub_region +
(1|Month) + (1|SiteID),
data=Trends.offshore)
#summary(DIC_of)
anova(DIC_of) ranova(DIC_of) step(DIC_of)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1695.8 3413.7
## (1 | SiteID) 1 10 -1695.8 3411.7 0.00 1 1
## (1 | Month) 0 9 -1757.9 3533.8 124.11 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 878.8 292.95 3 370.59 0.8509 0.466791
## Year 2 406.7 406.66 1 384.15 1.1825 0.277525
## Sub_region 0 4526.5 1508.84 3 374.98 4.3971 0.004676 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## DIC_umol_kg ~ Sub_region + (1 | Month)DIC_of<-lmer(DIC_umol_kg ~ Year + Sub_region + (1 | Month),
data =Trends.offshore)
summary(DIC_of)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DIC_umol_kg ~ Year + Sub_region + (1 | Month)
## Data: Trends.offshore
##
## REML criterion at convergence: 3400.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.8843 -0.4329 0.1397 0.6130 2.4102
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 243.8 15.61
## Residual 343.9 18.54
## Number of obs: 390, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 792.3190 1154.5069 384.1392 0.686 0.49295
## Year 0.6221 0.5721 384.1451 1.087 0.27753
## Sub_regionUK -2.9722 2.9870 373.5600 -0.995 0.32037
## Sub_regionMK -9.1133 2.9547 373.5576 -3.084 0.00219 **
## Sub_regionLK -1.3305 2.8028 373.7602 -0.475 0.63528
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.007 0.006
## Sub_reginMK -0.007 0.005 0.590
## Sub_reginLK -0.020 0.019 0.624 0.632 plot(DIC_of)
qqnorm(resid(DIC_of))
acf(resid(DIC_of), main="acf(resid(DIC Offshore))") 
DIC_of.emmc<-emmeans(DIC_of, ~Sub_region+Year)
DIC_of.emmc<-multcomp::cld(DIC_of.emmc)
DIC_of.emmc<-DIC_of.emmc[order(DIC_of.emmc$Sub_region),]
DIC_of.emmcOceanic
hist(Trends.ocean$DIC_umol_kg)
shapiro.test(Trends.ocean$DIC_umol_kg)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.ocean$DIC_umol_kg
## W = 0.92806, p-value = 8.447e-07DIC_oc<-lmer(DIC_umol_kg ~ Year * Sub_region +
(1 | Month)+ (1|SiteID),
data=Trends.ocean)
#summary(DIC_oc)
anova(DIC_oc) ranova(DIC_oc) step(DIC_oc)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 9 -602.93 1223.8
## (1 | SiteID) 1 8 -602.93 1221.8 0.00 1 1
## (1 | Month) 0 7 -620.68 1255.4 35.51 1 2.537e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 603.06 301.53 2 129.48 1.5503 0.216094
## Sub_region 2 283.86 141.93 2 131.53 0.7231 0.487149
## Year 0 2034.28 2034.28 1 145.68 10.4027 0.001554 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## DIC_umol_kg ~ Year + (1 | Month)DIC_oc<-lmer(DIC_umol_kg ~ Year+ (1 | Month), data = Trends.ocean)
summary(DIC_oc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DIC_umol_kg ~ Year + (1 | Month)
## Data: Trends.ocean
##
## REML criterion at convergence: 1222.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9710 -0.4501 0.0728 0.5284 5.6177
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 181.8 13.48
## Residual 195.6 13.98
## Number of obs: 148, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2368.5433 1369.1200 145.6809 -1.730 0.08575 .
## Year 2.1884 0.6785 145.6797 3.225 0.00155 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Year -1.000 plot(DIC_oc)
qqnorm(resid(DIC_oc))
acf(resid(DIC_oc), main="acf(resid(DIC open))")
DIC_oc.emmc<-emmeans(DIC_oc, ~Year)
DIC_oc.emmc<-multcomp::cld(DIC_oc.emmc)
#DIC_oc.emmc<-DIC_oc.emmc[order(DIC_oc.emmc$Sub_region),]
DIC_oc.emmcModels summary
DIC_models<-list("Inshore" = DIC_in,
"Mid channel" = DIC_mc,
"Offshore" = DIC_of,
"Ocean" = DIC_oc)
modelsummary(DIC_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'DIC model outputs',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | 0.272 | −1.574 | 0.622 | 2.188** |
| (2.412) | (1.546) | (0.572) | (0.679) | |
| Intercept | 1505.848 | 5223.535+ | 792.319 | −2368.543+ |
| (4868.176) | (3120.132) | (1154.507) | (1369.120) | |
| Upper Keys | −16.553 | −8.601 | −2.972 | |
| (18.664) | (8.196) | (2.987) | ||
| Middle Keys | −45.702* | −21.738** | −9.113** | |
| (18.536) | (7.268) | (2.955) | ||
| Lower Keys | −70.213*** | −24.593*** | −1.330 | |
| (18.524) | (7.256) | (2.803) | ||
| Num.Obs. | 347 | 319 | 390 | 148 |
| R2 Marg. | 0.053 | 0.028 | 0.022 | 0.054 |
| R2 Cond. | 0.605 | 0.468 | 0.428 | 0.510 |
| AIC | 3987.6 | 3357.6 | 3414.6 | 1230.9 |
| BIC | 4018.3 | 3384.0 | 3442.4 | 1242.9 |
| ICC | 0.6 | 0.5 | 0.4 | 0.5 |
| RMSE | 70.39 | 44.17 | 18.17 | 13.41 |
| + p |
#modelsummary(DIC_models, estimate = "p.value")dTA-All
Long_plot2<- ggplot(Reef.Trends.data) +
MyTheme +
#scale_shape_manual(values=c(24, 25, 21))+
#guides(fill=guide_legend(override.aes=list(shape=21)))+
theme(axis.title.x = element_blank())
##
## Shapiro-Wilk normality test
##
## data: Reef.Trends.data$dTA_MY
## W = 0.81508, p-value < 2.2e-16
Inshore
# Inshore dTA
hist(Trends.inshore$dTA_MY)
shapiro.test(Trends.inshore$dTA_MY)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.inshore$dTA_MY
## W = 0.97002, p-value = 1.519e-06dTA_in<-lmer(dTA_MY ~ Year * Sub_region + (1|Month)+
#(1|Zone)+
(1|SiteID),
data=Trends.inshore)
#summary(dTA_in)
anova(dTA_in) ranova(dTA_in) step(dTA_in)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1951.6 3925.2
## (1 | SiteID) 1 10 -1952.7 3925.3 2.072 1 0.15
## (1 | Month) 0 9 -2032.8 4083.6 160.287 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 25925 8642 3 325.00 1.6620 0.1751
## Year 2 1403 1403 1 339.00 0.2682 0.6048
## Sub_region 0 561528 187176 3 329.04 35.8352 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## dTA_MY ~ Sub_region + (1 | Month)dTA_in<-lmer(dTA_MY ~ Sub_region + (1 | Month),
data=Trends.inshore)
summary(dTA_in) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dTA_MY ~ Sub_region + (1 | Month)
## Data: Trends.inshore
##
## REML criterion at convergence: 3929
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4682 -0.5758 0.0229 0.5605 4.7357
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 4683 68.43
## Residual 5223 72.27
## Number of obs: 344, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.565 21.742 14.649 -0.210 0.83660
## Sub_regionUK 3.105 11.930 328.969 0.260 0.79483
## Sub_regionMK -32.734 11.676 328.953 -2.804 0.00535 **
## Sub_regionLK -95.634 11.569 329.095 -8.266 3.44e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sb_rUK Sb_rMK
## Sub_reginUK -0.311
## Sub_reginMK -0.318 0.579
## Sub_reginLK -0.322 0.582 0.599 plot(dTA_in)
qqnorm(resid(dTA_in))
acf(resid(dTA_in), main="acf(resid(dTA Inshore))")
dTA_in.emmc<-emmeans(dTA_in, ~Sub_region)
dTA_in.emmc<-multcomp::cld(dTA_in.emmc)
dTA_in.emmc<-dTA_in.emmc[order(dTA_in.emmc$Sub_region),]
dTA_in.emmcMid channel
hist(Trends.midchanel$dTA_MY)
shapiro.test(Trends.midchanel$dTA_MY)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.midchanel$dTA_MY
## W = 0.84667, p-value < 2.2e-16dTA_mc<-lmer(dTA_MY~Year * Sub_region + (1|Month) +
#(1|Zone)+
(1|SiteID),
data=Trends.midchanel)
#summary(dTA_mc)
anova(dTA_mc) ranova(dTA_mc) step(dTA_mc)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1634.9 3291.9
## (1 | Month) 0 10 -1675.6 3371.1 81.244 1 < 2e-16 ***
## (1 | SiteID) 0 10 -1636.6 3293.2 3.278 1 0.07022 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 6569.8 2189.9 3 292.15 1.2103 0.30620
## Year 2 3286.6 3286.6 1 303.68 1.8119 0.17929
## Sub_region 0 26985.2 8995.1 3 6.28 4.9261 0.04385 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## dTA_MY ~ Sub_region + (1 | Month) + (1 | SiteID)dTA_mc<-lmer(dTA_MY ~ (1 | Month) + (1|SiteID),
data = Trends.midchanel)
summary(dTA_mc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dTA_MY ~ (1 | Month) + (1 | SiteID)
## Data: Trends.midchanel
##
## REML criterion at convergence: 3319.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.9402 -0.4290 0.1099 0.5807 3.3885
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 889.6 29.83
## SiteID (Intercept) 256.2 16.01
## Residual 1826.4 42.74
## Number of obs: 317, groups: Month, 12; SiteID, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -17.84 10.30 15.90 -1.732 0.103 plot(dTA_mc)
qqnorm(resid(dTA_mc))
acf(resid(dTA_mc), main="acf(resid(dTA Mid Channel))") 
Offshore
hist(Trends.offshore$dTA_MY)
shapiro.test(Trends.offshore$dTA_MY)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.offshore$dTA_MY
## W = 0.82231, p-value < 2.2e-16dTA_of<-lmer(dTA_MY~Year * Sub_region +
(1|Month) + (1|SiteID),
data=Trends.offshore)
#summary(dTA_of)
anova(dTA_of) ranova(dTA_of) step(dTA_of)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1614.1 3250.2
## (1 | SiteID) 1 10 -1614.1 3248.2 0.000 1 1
## (1 | Month) 0 9 -1639.0 3296.0 49.792 1 1.71e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 201.09 67.03 3 366.21 0.2673 0.84896
## Year 0 1155.22 1155.22 1 352.17 4.6342 0.03202 *
## Sub_region 0 2796.86 932.29 3 369.55 3.7399 0.01137 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## dTA_MY ~ Year + Sub_region + (1 | Month)dTA_of<-lmer(dTA_MY ~ Sub_region + (1 | Month),
data =Trends.offshore)
summary(dTA_of) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dTA_MY ~ Sub_region + (1 | Month)
## Data: Trends.offshore
##
## REML criterion at convergence: 3239.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.6287 -0.3153 0.0989 0.5356 3.7481
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 57.45 7.579
## Residual 253.21 15.913
## Number of obs: 386, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.8304 2.9701 27.2451 -0.616 0.5428
## Sub_regionUK 0.6779 2.5913 370.9069 0.262 0.7938
## Sub_regionMK -5.7041 2.5692 370.9256 -2.220 0.0270 *
## Sub_regionLK -4.4715 2.4280 371.6279 -1.842 0.0663 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sb_rUK Sb_rMK
## Sub_reginUK -0.514
## Sub_reginMK -0.520 0.594
## Sub_reginLK -0.551 0.629 0.637 plot(dTA_of)
qqnorm(resid(dTA_of))
acf(resid(dTA_of), main="acf(resid(dTA Offshore))") 
dTA_of.emmc<-emmeans(dTA_of, ~Sub_region)
dTA_of.emmc<-multcomp::cld(dTA_of.emmc)
dTA_of.emmc<-dTA_of.emmc[order(dTA_of.emmc$Sub_region),]
dTA_of.emmcModels summary
dTA_models<-list("Inshore" = dTA_in,
"Mid channel" = dTA_mc,
"Offshore" = dTA_of)
modelsummary(dTA_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'dTA model outputs',
coef_map=factor_order)| Inshore | Mid channel | Offshore | |
|---|---|---|---|
| Intercept | −4.565 | −17.845+ | −1.830 |
| (21.742) | (10.304) | (2.970) | |
| Upper Keys | 3.105 | 0.678 | |
| (11.930) | (2.591) | ||
| Middle Keys | −32.734** | −5.704* | |
| (11.676) | (2.569) | ||
| Lower Keys | −95.634*** | −4.471+ | |
| (11.569) | (2.428) | ||
| Num.Obs. | 344 | 317 | 386 |
| R2 Marg. | 0.144 | 0.000 | 0.023 |
| R2 Cond. | 0.549 | 0.386 | 0.204 |
| AIC | 3941.0 | 3327.4 | 3251.1 |
| BIC | 3964.0 | 3342.4 | 3274.8 |
| ICC | 0.5 | 0.4 | 0.2 |
| RMSE | 70.73 | 41.47 | 15.63 |
| + p |
#modelsummary(dTA_models, estimate = "p.value")dDIC-All
##
## Shapiro-Wilk normality test
##
## data: Reef.Trends.data$dDIC_MY
## W = 0.8734, p-value < 2.2e-16
Inshore
# Inshore dDIC
hist(Trends.inshore$dDIC_MY)
shapiro.test(Trends.inshore$dDIC_MY)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.inshore$dDIC_MY
## W = 0.99413, p-value = 0.2125dDIC_in<-lmer(dDIC_MY~Year * Sub_region + (1|Month)+
#(1|Zone)+
(1|SiteID),
data=Trends.inshore)
#summary(dDIC_in)
anova(dDIC_in) ranova(dDIC_in) step(dDIC_in)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1935.0 3891.9
## (1 | Month) 0 10 -2046.1 4112.3 222.335 1 < 2e-16 ***
## (1 | SiteID) 0 10 -1937.3 3894.6 4.699 1 0.03018 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 25663 8554.4 3 315.69 1.7009 0.16674
## Year 2 3283 3282.5 1 329.01 0.6483 0.42130
## Sub_region 0 93964 31321.3 3 7.21 6.1908 0.02108 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## dDIC_MY ~ Sub_region + (1 | Month) + (1 | SiteID)dDIC_in<-lmer(dDIC_MY ~ Sub_region + (1 | Month)+ (1 | SiteID),
data=Trends.inshore)
summary(dDIC_in) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dDIC_MY ~ Sub_region + (1 | Month) + (1 | SiteID)
## Data: Trends.inshore
##
## REML criterion at convergence: 3894
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1776 -0.6750 0.0123 0.6505 4.0667
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 6259.2 79.11
## SiteID (Intercept) 260.7 16.15
## Residual 5059.3 71.13
## Number of obs: 341, groups: Month, 12; SiteID, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.522 27.054 16.492 0.315 0.75672
## Sub_regionUK -16.218 18.850 7.274 -0.860 0.41705
## Sub_regionMK -46.201 18.691 7.036 -2.472 0.04255 *
## Sub_regionLK -71.441 18.659 6.986 -3.829 0.00649 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sb_rUK Sb_rMK
## Sub_reginUK -0.409
## Sub_reginMK -0.413 0.592
## Sub_reginLK -0.413 0.592 0.598 plot(dDIC_in)
qqnorm(resid(dDIC_in))
acf(resid(dDIC_in), main="acf(resid(dDIC Inshore))") 
dDIC_in.emmc<-emmeans(dDIC_in, ~Sub_region)
dDIC_in.emmc<-multcomp::cld(dDIC_in.emmc)
dDIC_in.emmc<-dDIC_in.emmc[order(dDIC_in.emmc$Sub_region),]
dDIC_in.emmcMid channel
hist(Trends.midchanel$dDIC_MY)
shapiro.test(Trends.midchanel$dDIC_MY)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.midchanel$dDIC_MY
## W = 0.90416, p-value = 3.03e-13dDIC_mc<-lmer(dDIC_MY~Year * Sub_region + (1|Month) +
#(1|Zone)+
(1|SiteID), data=Trends.midchanel)
#summary(dDIC_mc)
anova(dDIC_mc) ranova(dDIC_mc) step(dDIC_mc)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1629.6 3281.1
## (1 | SiteID) 1 10 -1630.5 3281.0 1.855 1 0.1732
## (1 | Month) 0 9 -1687.7 3393.4 114.405 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 9224 3074.6 3 296.19 1.6154 0.185850
## Year 0 12325 12324.9 1 308.75 6.4331 0.011694 *
## Sub_region 0 31760 10586.6 3 299.28 5.5258 0.001051 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## dDIC_MY ~ Year + Sub_region + (1 | Month)dDIC_mc<-lmer(dDIC_MY ~ Sub_region + (1 | Month),
data = Trends.midchanel)
summary(dDIC_mc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dDIC_MY ~ Sub_region + (1 | Month)
## Data: Trends.midchanel
##
## REML criterion at convergence: 3286.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3657 -0.4740 0.0462 0.5774 3.8614
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 1133 33.67
## Residual 1955 44.22
## Number of obs: 315, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.6643 11.2377 17.2116 0.059 0.953544
## Sub_regionUK -9.9279 8.0964 300.2499 -1.226 0.221076
## Sub_regionMK -22.7237 7.1784 300.2235 -3.166 0.001707 **
## Sub_regionLK -25.5581 7.1469 300.3309 -3.576 0.000406 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sb_rUK Sb_rMK
## Sub_reginUK -0.341
## Sub_reginMK -0.387 0.534
## Sub_reginLK -0.388 0.537 0.608 plot(dDIC_mc) 
qqnorm(resid(dDIC_mc))
acf(resid(dDIC_mc), main="acf(resid(dDIC Mid Channel))") 
dDIC_mc.emmc<-emmeans(dDIC_mc, ~Sub_region)
dDIC_mc.emmc<-multcomp::cld(dDIC_mc.emmc)
dDIC_mc.emmc<-dDIC_mc.emmc[order(dDIC_mc.emmc$Sub_region),]
dDIC_mc.emmcOffshore
hist(Trends.offshore$dDIC_MY)
shapiro.test(Trends.offshore$dDIC_MY)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.offshore$dDIC_MY
## W = 0.79373, p-value < 2.2e-16dDIC_of<-lmer(dDIC_MY~Year * Sub_region +
(1|Month) + (1|SiteID),
data=Trends.offshore)
#summary(dDIC_of)
anova(dDIC_of) ranova(dDIC_of) step(dDIC_of)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1604.0 3230.1
## (1 | SiteID) 1 10 -1604.0 3228.1 0.000 1 1
## (1 | Month) 0 9 -1619.4 3256.8 30.659 1 3.076e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 981.6 327.21 3 366.36 1.3246 0.2660682
## Year 0 2822.4 2822.40 1 297.58 11.3951 0.0008342 ***
## Sub_region 0 3746.4 1248.80 3 369.70 5.0419 0.0019546 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## dDIC_MY ~ Year + Sub_region + (1 | Month)dDIC_of<-lmer(dDIC_MY ~ Year+ Sub_region + (1 | Month),
data =Trends.offshore)
summary(dDIC_of) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dDIC_MY ~ Year + Sub_region + (1 | Month)
## Data: Trends.offshore
##
## REML criterion at convergence: 3217.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.1598 -0.3321 0.1429 0.5680 3.7397
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 38.55 6.209
## Residual 247.69 15.738
## Number of obs: 385, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3154.4511 934.3978 297.5305 3.376 0.000833 ***
## Year -1.5631 0.4631 297.5800 -3.376 0.000834 ***
## Sub_regionUK -2.7575 2.5629 369.2796 -1.076 0.282660
## Sub_regionMK -8.6040 2.5410 369.3161 -3.386 0.000785 ***
## Sub_regionLK -1.7258 2.4011 369.9717 -0.719 0.472754
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.008 0.006
## Sub_reginMK -0.007 0.005 0.594
## Sub_reginLK -0.019 0.018 0.630 0.636 plot(dDIC_of)
qqnorm(resid(dDIC_of))
acf(resid(dDIC_of), main="acf(resid(dDIC Offshore))") 
dDIC_of.emmc<-emmeans(dDIC_of, ~Sub_region+Year)
dDIC_of.emmc<-multcomp::cld(dDIC_of.emmc)
dDIC_of.emmc<-dDIC_of.emmc[order(dDIC_of.emmc$Sub_region),]
dDIC_of.emmcModels summary
dDIC_models<-list("Inshore" = dDIC_in,
"Mid channel" = dDIC_mc,
"Offshore" = dDIC_of)
modelsummary(dDIC_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'dDIC model outputs',
coef_map=factor_order)| Inshore | Mid channel | Offshore | |
|---|---|---|---|
| Year | −1.563*** | ||
| (0.463) | |||
| Intercept | 8.522 | 0.664 | 3154.451*** |
| (27.054) | (11.238) | (934.398) | |
| Upper Keys | −16.218 | −9.928 | −2.757 |
| (18.850) | (8.096) | (2.563) | |
| Middle Keys | −46.201* | −22.724** | −8.604*** |
| (18.691) | (7.178) | (2.541) | |
| Lower Keys | −71.441*** | −25.558*** | −1.726 |
| (18.659) | (7.147) | (2.401) | |
| Num.Obs. | 341 | 315 | 385 |
| R2 Marg. | 0.059 | 0.031 | 0.067 |
| R2 Cond. | 0.589 | 0.387 | 0.193 |
| AIC | 3908.0 | 3298.9 | 3231.5 |
| BIC | 3934.8 | 3321.4 | 3259.1 |
| ICC | 0.6 | 0.4 | 0.1 |
| RMSE | 69.12 | 43.21 | 15.45 |
| + p |
#modelsummary(dDIC_models, estimate = "p.value")Figure 2: Trends
Raw data lm
Trends_H<-ggarrange(Trends_a+ theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
legend.position = "none"),
Trends_b+ theme(legend.position = "none",
axis.title.x=element_blank(),
axis.text.x=element_blank(),
strip.text.x = element_blank()),
Trends_c + theme(legend.position = "bottom",
strip.text.x = element_blank()),
labels = c("A", "B", "C"),
ncol = 1, nrow = 3, heights = c(0.9, 0.9, 1.2))
Trends_H
Trends_H2<-ggarrange(Trends_a2+ theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
legend.position = "none"),
Trends_b2+ theme(legend.position = "none",
axis.title.x=element_blank(),
axis.text.x=element_blank(),
strip.text.x = element_blank()),
Trends_c2 + theme(legend.position = "bottom",
strip.text.x = element_blank()),
labels = c("A", "B", "C"),
ncol = 1, nrow = 3, heights = c(0.9, 0.9, 1.2))
Trends_H2
TrendsV<-ggarrange(Trends_a+
facet_grid(Zone~., scales = "free_y")+
theme(legend.position = "none",
panel.spacing = unit(0.6, "cm")),
Trends_b+
facet_grid(Zone~., scales = "free_y")+
theme(legend.position = "none",
panel.spacing = unit(0.6, "cm")),
Trends_c +
facet_grid(Zone~., scales = "free_y")+
theme(legend.position = "none",
panel.spacing = unit(0.6, "cm")),
labels = c("A", "B", "C"),
ncol = 3, nrow = 1)
#TrendsV
#ggsave(file="FLK_results/Fig_2_Trends.svg", plot=TrendsH, dpi = 300, width=7.5, height=7)Figure 2: Ocean acidification trends in the Florida Reef Tract. (A) The partial pressure of the CO2, (B) pH, and (C) Aragonite saturation state (Ωar). The colors represent the biogeographical regions and the panels separate the locations by shelf zone. Text on the top of each panel shows the coefficient of change in the parameter per year (for significant trends) and the significance of the region in the model. Note the different scales between the top and bottom panels.
Trends_Modeled<-ggarrange(
Trends_pCO2,
Trends_pH,
Trends_Omega,
#labels = c("A", "B", "C"),
ncol = 1, nrow = 3, heights = c(1.1, 0.9, 1.1))
Trends_Modeled
#ggsave(file="FLK_results/Fig_2_Trends.svg", plot=Trends_Modeled, dpi = 300, width=7.5, height=7)
#ggsave(file="FLK_results/Fig_2_Trends_ICRS.svg", plot=Trends_Modeled, dpi = 300, width=9, height=5)Figure 2: Ocean acidification trends in the Florida Reef Tract. (A) The partial pressure of the CO2, (B) pH, and (C) Aragonite saturation state (Ωar). The colors represent the biogeographical regions and the panels separate the locations by shelf zone. Text on the top of each panel shows the coefficient of change in the parameter per year (for significant trends). Solid lines represent models with significant trends (year<0.05). Multiple coloured lines are depicted for models where region was significant (region p<0.05). Dashed lines represent the intercepts of models with non significant trends (year p>0.05).
7. PCA
#str(Month_Stats)
PCA_variables<-c("DIC_mean", "TA_mean", "pCO2_mean", "Ara_mean",
"pH_mean", "Tem_mean","Sal_mean", "Season",
"Zone", "Sub_region", "Month2")
PCA.data<-select(Month_Stats, PCA_variables)
Name<-paste(Month_Stats$Zone, Month_Stats$Sub_region,
Month_Stats$Month, sep = "_")
rownames(PCA.data) <- (Name)
PCA_var.names<-c("DIC", "TA", "pCO2", "Ara",
"pH", "Temp","Sal", "Season",
"Zone", "Sub_region", "Month")
colnames(PCA.data) <- (PCA_var.names)
#ggpairs(PCA.data)head(PCA.data)library(vegan)
#library(autoplot)
library(ggfortify)
CC.rda1 <- rda(PCA.data[, -(6:11)], scale=TRUE)
CC.rda2 <- prcomp(PCA.data[, -(6:11)], scale=TRUE)
summary(CC.rda1)##
## Call:
## rda(X = PCA.data[, -(6:11)], scale = TRUE)
##
## Partitioning of correlations:
## Inertia Proportion
## Total 5 1
## Unconstrained 5 1
##
## Eigenvalues, and their contribution to the correlations
##
## Importance of components:
## PC1 PC2 PC3 PC4 PC5
## Eigenvalue 2.55 1.9602 0.47883 0.008886 0.0019029
## Proportion Explained 0.51 0.3920 0.09577 0.001777 0.0003806
## Cumulative Proportion 0.51 0.9021 0.99784 0.999619 1.0000000
##
## Scaling 2 for species and site scores
## * Species are scaled proportional to eigenvalues
## * Sites are unscaled: weighted dispersion equal on all dimensions
## * General scaling constant of scores: 5.469602
##
##
## Species scores
##
## PC1 PC2 PC3 PC4 PC5
## DIC -0.05552 2.4431 -0.07537 -0.007863 0.07452
## TA 1.07169 2.1249 -0.56043 0.029789 -0.06861
## pCO2 -2.34755 0.2331 -0.62637 -0.159397 -0.01484
## Ara 1.74079 -1.0840 -1.33302 -0.012281 0.02983
## pH 2.35909 0.1240 0.61315 -0.163273 -0.00386
##
##
## Site scores (weighted sums of species scores)
##
## PC1 PC2 PC3 PC4 PC5
## Inshore_BB_Jan -0.080069 0.7539854 0.456391 0.092977 0.159036
## Inshore_BB_Feb 0.417307 0.4512656 0.364161 -0.210958 0.215637
## Inshore_BB_Mar 0.492968 0.3248128 0.043874 -0.280525 0.024573
## Inshore_BB_Apr 0.717303 -0.5103336 0.087995 -0.541087 -0.282290
## Inshore_BB_May 0.647383 -0.3846725 -0.151919 -0.447432 -0.132122
## Inshore_BB_Jun -0.029219 -0.8499796 0.479656 0.392550 0.150100
## Inshore_BB_Jul -0.504001 -0.3190959 -0.177733 0.234274 -0.382684
## Inshore_BB_Aug -0.497922 -0.2070975 -0.503920 -0.557451 -0.391026
## Inshore_BB_Sep -1.086319 0.3547316 -0.344368 -1.669542 -0.412421
## Inshore_BB_Oct -1.098578 0.7012590 -0.366387 -1.447755 -0.606811
## Inshore_BB_Nov -0.372862 0.6687206 -0.037990 -0.100462 -0.194547
## Inshore_BB_Dec -0.119021 0.6404513 0.375628 0.091136 0.167365
## Inshore_UK_Jan 0.124641 0.8896160 0.342349 -0.102604 0.050070
## Inshore_UK_Feb 0.680088 0.1397443 0.345466 -0.614502 0.066163
## Inshore_UK_Mar 0.610439 0.1660965 0.001473 -0.372967 -0.345958
## Inshore_UK_Apr 1.216231 -1.0742692 -0.066480 -1.800223 -0.705026
## Inshore_UK_May 0.996427 -0.9178564 -0.114166 -0.998742 -0.590887
## Inshore_UK_Jun 0.295848 -0.5107352 -0.155382 0.232370 0.005968
## Inshore_UK_Jul -0.210552 -0.2751376 -0.471386 0.178738 -0.125705
## Inshore_UK_Aug 0.142293 -0.3725017 -0.802168 0.283018 0.009227
## Inshore_UK_Sep -0.924098 -0.0699711 -0.447643 -0.888534 -0.252864
## Inshore_UK_Oct -1.045798 0.5023971 -0.315999 -1.396787 -0.551630
## Inshore_UK_Nov -0.769913 1.0353953 -0.219340 -1.035345 -0.912475
## Inshore_UK_Dec -0.145236 0.9323143 0.114997 -0.049600 -0.233427
## Inshore_MK_Jan 0.056671 1.1595207 0.037518 0.001205 -0.132359
## Inshore_MK_Feb 0.807850 0.4648431 0.033851 -0.980542 0.705563
## Inshore_MK_Mar 0.491864 0.2272537 -0.027678 -0.171228 -0.400813
## Inshore_MK_Apr 0.940584 -0.6868600 -0.061836 -1.023662 -0.538141
## Inshore_MK_May 0.947519 -1.0268837 0.013390 -1.046979 -0.698433
## Inshore_MK_Jun 0.160732 -1.0590595 0.306574 0.116315 -0.047826
## Inshore_MK_Jul -0.683490 -1.2434164 0.744609 0.716768 -0.653362
## Inshore_MK_Aug -0.321595 -0.8943156 0.265868 0.608883 -0.019540
## Inshore_MK_Sep -0.888855 -0.8852520 0.380960 0.274261 -0.419462
## Inshore_MK_Oct -0.556382 0.1184323 -0.097186 -0.551056 0.287254
## Inshore_MK_Nov -0.275140 0.1491419 0.184164 0.208480 -0.164513
## Inshore_MK_Dec -0.061125 0.8554369 0.225455 -0.027144 0.036211
## Inshore_LK_Jan -0.564659 0.5926086 0.963478 0.171954 -0.216286
## Inshore_LK_Feb 0.267351 0.5358781 0.380421 -0.159304 0.380583
## Inshore_LK_Mar 0.440663 0.0346635 0.267776 -0.332876 -0.190229
## Inshore_LK_Apr 0.378949 -0.7642832 0.585728 -0.246506 0.161826
## Inshore_LK_May 0.213205 -1.2463711 1.107684 0.095769 -0.034456
## Inshore_LK_Jun -0.299728 -0.9063526 0.600088 0.381431 -0.139592
## Inshore_LK_Jul -1.171787 -0.8595304 0.746376 0.117162 -0.625488
## Inshore_LK_Aug -1.414780 -1.0262726 0.793940 -1.631919 3.073166
## Inshore_LK_Sep -1.308589 -0.7902957 0.637666 -0.438920 -0.438266
## Inshore_LK_Oct -0.968443 -0.2850522 0.649046 -0.314838 0.008713
## Inshore_LK_Nov -0.881095 0.2884499 0.442489 -0.022094 -0.815052
## Inshore_LK_Dec -0.324311 0.1676589 1.000853 0.288712 0.202920
## Mid channel_BB_Jan 0.081080 0.2686107 0.419206 0.200639 0.051901
## Mid channel_BB_Feb 0.172030 0.2762156 0.501836 0.098141 0.173454
## Mid channel_BB_Mar 0.297450 0.1910812 0.270365 0.089281 -0.114827
## Mid channel_BB_Apr 0.346015 -0.0659565 0.100144 0.036608 -0.119536
## Mid channel_BB_May 0.328616 0.0360189 -0.132946 0.118926 -0.106348
## Mid channel_BB_Jun 0.031238 -0.1684818 -0.151700 0.248731 0.177683
## Mid channel_BB_Jul -0.193232 0.0008191 -0.532947 0.188811 -0.123082
## Mid channel_BB_Aug -0.261570 -0.1024514 -0.574914 0.211539 -0.200440
## Mid channel_BB_Sep -0.531528 0.0445564 -0.251237 -0.225172 0.238720
## Mid channel_BB_Oct -0.344148 0.1881645 -0.303637 -0.085824 0.175603
## Mid channel_BB_Nov -0.088935 0.1948601 0.035985 0.169334 -0.146176
## Mid channel_BB_Dec -0.005996 0.3620453 0.289420 0.185511 0.038331
## Mid channel_UK_Jan 0.226642 0.3290768 0.338663 0.054950 0.128544
## Mid channel_UK_Feb 0.352518 0.1527601 0.526416 -0.125459 0.287610
## Mid channel_UK_Mar 0.442570 -0.0150254 0.245445 -0.172447 -0.123566
## Mid channel_UK_Apr 0.733488 -0.1822489 -0.176963 -0.978369 0.786551
## Mid channel_UK_May 0.680829 -0.1604492 -0.350116 -0.412290 -0.362833
## Mid channel_UK_Jun 0.124555 -0.0829247 -0.405799 0.249648 -0.074828
## Mid channel_UK_Jul -0.159177 -0.0788420 -0.537187 0.185357 0.033849
## Mid channel_UK_Aug 0.117709 0.0100999 -0.936753 0.245747 -0.065856
## Mid channel_UK_Sep -0.103033 -0.1873534 -0.672088 0.078761 0.538251
## Mid channel_UK_Oct -0.328536 0.1444733 -0.364662 -0.126435 0.333851
## Mid channel_UK_Nov -0.196957 0.0529116 0.183926 0.201945 0.140793
## Mid channel_UK_Dec 0.046917 0.3443033 0.267758 0.202378 -0.021879
## Mid channel_MK_Jan 0.024115 0.6111878 0.206298 0.134615 -0.109445
## Mid channel_MK_Feb 0.378998 0.2072672 0.438841 -0.149551 0.235686
## Mid channel_MK_Mar 0.449242 0.1527225 0.168722 -0.218612 -0.280383
## Mid channel_MK_Apr 0.590716 -0.1395414 -0.147408 -0.201610 -0.212645
## Mid channel_MK_May 0.580328 -0.4817111 -0.087733 -0.325459 -0.343832
## Mid channel_MK_Jun 0.017045 -0.5683920 0.019343 0.363207 0.069526
## Mid channel_MK_Jul -0.371417 -0.5815118 -0.070043 0.452588 -0.093172
## Mid channel_MK_Aug -0.023282 -0.4187343 -0.444991 0.397030 0.093777
## Mid channel_MK_Sep -0.659454 -0.4936600 0.051944 0.103974 0.162873
## Mid channel_MK_Oct -0.156086 0.0993805 -0.377652 -0.023100 0.414989
## Mid channel_MK_Nov -0.067639 0.0238673 0.086653 0.224022 0.090274
## Mid channel_MK_Dec -0.020845 0.4700438 0.306279 0.108841 0.013854
## Mid channel_LK_Jan -0.360370 0.4936598 0.687886 0.144566 0.040060
## Mid channel_LK_Feb 0.288601 0.4807089 0.300610 -0.118979 0.180225
## Mid channel_LK_Mar 0.221424 0.2439356 0.203512 0.075293 -0.043652
## Mid channel_LK_Apr 0.146310 -0.1484986 0.161059 -0.236676 -0.192959
## Mid channel_LK_May 0.208481 -0.6734195 0.614467 0.219229 -0.088080
## Mid channel_LK_Jun -0.077433 -0.2935608 -0.032655 0.322784 -0.072121
## Mid channel_LK_Jul -0.395927 -0.2723662 -0.282504 0.231468 -0.240295
## Mid channel_LK_Aug -0.386762 -0.5245157 -0.034230 0.482337 -1.948842
## Mid channel_LK_Sep -0.605622 -0.5893130 0.157691 0.182188 0.302359
## Mid channel_LK_Oct -0.271153 0.0164411 -0.218705 0.050044 0.303262
## Mid channel_LK_Nov -0.206202 0.0596093 0.118979 0.210486 -0.106739
## Mid channel_LK_Dec 0.016178 0.2075496 0.392785 0.188980 0.073025
## Offshore_BB_Jan 0.123896 0.1796639 0.263640 0.131838 0.217052
## Offshore_BB_Feb 0.267615 0.1091596 0.529314 0.004660 0.214283
## Offshore_BB_Mar 0.280004 0.1430616 0.267855 0.102249 -0.121700
## Offshore_BB_Apr 0.081134 0.1632253 0.094879 0.236322 -0.122974
## Offshore_BB_May 0.188581 0.1023062 -0.076094 0.164471 0.004170
## Offshore_BB_Jun -0.149182 0.0742131 -0.244706 0.196786 -0.139650
## Offshore_BB_Jul -0.096466 0.0149908 -0.588901 0.152181 0.084406
## Offshore_BB_Aug -0.386330 -0.0023998 -0.548027 0.073485 -0.355059
## Offshore_BB_Sep -0.196291 -0.0434459 -0.578138 -0.232204 0.443955
## Offshore_BB_Oct -0.083439 0.0213190 -0.376053 0.056808 0.452259
## Offshore_BB_Nov 0.108313 0.0377352 -0.078493 0.253121 -0.122264
## Offshore_BB_Dec 0.050673 0.2043999 0.196412 0.167071 0.044340
## Offshore_UK_Jan 0.153231 0.1895434 0.277973 0.179573 0.031052
## Offshore_UK_Feb 0.252026 0.1914798 0.594766 -0.031192 0.325287
## Offshore_UK_Mar 0.235913 0.1557187 0.237807 0.161064 -0.115577
## Offshore_UK_Apr 0.101747 0.1405046 0.114217 0.204263 -0.081670
## Offshore_UK_May 0.148670 0.1588312 -0.126341 0.227157 -0.186724
## Offshore_UK_Jun -0.055031 0.0344786 -0.297952 0.266646 -0.151867
## Offshore_UK_Jul -0.146794 -0.0691797 -0.546540 0.207404 -0.022434
## Offshore_UK_Aug 0.040746 0.0088196 -0.829930 0.255412 -0.110743
## Offshore_UK_Sep -0.026777 -0.1877966 -0.674391 0.089980 0.507719
## Offshore_UK_Oct 0.013585 0.0094032 -0.385492 0.069518 0.512075
## Offshore_UK_Nov -0.033260 -0.0170553 0.033489 0.286272 0.064600
## Offshore_UK_Dec 0.036311 0.2651256 0.161379 0.185996 -0.031561
## Offshore_MK_Jan 0.063649 0.2428240 0.311331 0.223912 0.019689
## Offshore_MK_Feb 0.265526 0.2110340 0.650496 -0.060106 0.346261
## Offshore_MK_Mar 0.207268 0.1858139 0.244845 0.180470 -0.146356
## Offshore_MK_Apr 0.218314 0.0789399 0.072401 0.155750 -0.098735
## Offshore_MK_May 0.123729 0.1077204 -0.090528 0.209279 -0.177057
## Offshore_MK_Jun 0.094489 -0.1525861 -0.227734 0.273361 0.025093
## Offshore_MK_Jul -0.193138 -0.2286722 -0.352228 0.238943 0.098318
## Offshore_MK_Aug 0.021478 -0.0767230 -0.693046 0.254532 0.020091
## Offshore_MK_Sep -0.117051 -0.3212203 -0.364855 0.194320 0.513333
## Offshore_MK_Oct -0.014283 0.0291906 -0.422940 0.036137 0.519225
## Offshore_MK_Nov 0.175570 -0.0473526 -0.121210 0.214700 0.068938
## Offshore_MK_Dec 0.088645 0.1565409 0.252194 0.138171 0.109288
## Offshore_LK_Jan 0.058970 0.2857051 0.411977 0.228382 0.007019
## Offshore_LK_Feb 0.167616 0.2767659 0.717416 -0.015907 0.371277
## Offshore_LK_Mar 0.157992 0.1295156 0.195678 0.206924 -0.165108
## Offshore_LK_Apr -0.031739 0.1212983 0.186456 0.209965 -0.181020
## Offshore_LK_May 0.020411 0.1114056 0.082260 0.316359 -0.200566
## Offshore_LK_Jun -0.087593 0.0057823 -0.239096 0.288915 -0.141305
## Offshore_LK_Jul -0.261641 0.0329310 -0.542811 0.126061 -0.199980
## Offshore_LK_Aug -0.179853 0.0443852 -0.617626 0.172932 -0.312085
## Offshore_LK_Sep -0.097682 -0.2162602 -0.352073 0.097404 0.608239
## Offshore_LK_Oct -0.090081 0.0428623 -0.318595 0.115525 0.308925
## Offshore_LK_Nov 0.030796 0.0269884 -0.034803 0.288337 -0.093536
## Offshore_LK_Dec 0.122370 0.1400435 0.210843 0.078730 0.176844
## Oceanic_UK_Jan 0.127014 0.1203089 0.156126 0.192266 0.084697
## Oceanic_UK_Feb 0.269915 0.1867839 0.588315 -0.008817 0.169718
## Oceanic_UK_Mar 0.207466 0.0518943 0.284912 0.155393 0.029800
## Oceanic_UK_Apr 0.237694 0.0749999 0.199711 0.106444 0.069566
## Oceanic_UK_May 0.310533 0.1071029 -0.110880 0.168519 -0.192264
## Oceanic_UK_Jun 0.091701 0.0375338 -0.367460 0.252405 -0.097140
## Oceanic_UK_Jul -0.024223 -0.0796508 -0.587094 0.201507 0.149123
## Oceanic_UK_Aug 0.139307 0.1326737 -0.932162 0.242891 -0.207574
## Oceanic_UK_Sep 0.140378 -0.2266276 -0.631314 0.033277 0.885965
## Oceanic_UK_Oct -0.045900 0.0015541 -0.450922 0.060193 0.440763
## Oceanic_UK_Nov 0.044888 -0.0417239 0.051290 0.195380 0.328576
## Oceanic_UK_Dec 0.124658 0.1845401 0.074372 0.158960 -0.060913
## Oceanic_MK_Jan 0.133419 0.1804947 0.259521 0.181455 0.062168
## Oceanic_MK_Feb -0.046636 0.2930968 0.545091 -0.251298 -0.037581
## Oceanic_MK_Mar 0.128234 0.0998006 0.184472 0.248542 -0.086926
## Oceanic_MK_Apr -0.444192 0.3161560 0.020303 -1.047040 -0.918255
## Oceanic_MK_May 0.043353 0.2063294 -0.097894 0.228583 -0.181682
## Oceanic_MK_Jun 0.061000 0.0301871 -0.376947 0.266332 -0.159031
## Oceanic_MK_Jul -0.033099 -0.0588004 -0.580371 0.166671 0.189668
## Oceanic_MK_Aug 0.014009 0.0507476 -0.756578 0.191601 0.025759
## Oceanic_MK_Sep 0.144692 -0.1384619 -0.633486 0.082715 0.668979
## Oceanic_MK_Oct -0.026554 0.0704968 -0.512717 0.076375 0.296927
## Oceanic_MK_Nov 0.187863 -0.0165563 -0.194944 0.264154 -0.131242
## Oceanic_MK_Dec 0.071788 0.1274158 0.229944 0.147423 0.113302
## Oceanic_LK_Jan 0.113815 0.2106856 0.321129 0.223068 -0.040646
## Oceanic_LK_Feb 0.313149 0.1972388 0.535522 -0.057188 0.147272
## Oceanic_LK_Mar 0.142936 0.0709909 0.149903 0.226718 -0.065188
## Oceanic_LK_Apr -0.013182 0.1344065 0.143877 0.259811 -0.084268
## Oceanic_LK_May 0.040519 0.1780986 -0.002768 0.285668 -0.182649
## Oceanic_LK_Jun 0.017238 0.0612943 -0.344613 0.244930 -0.148788
## Oceanic_LK_Jul -0.084127 -0.0466388 -0.462392 0.120568 0.370429
## Oceanic_LK_Aug -0.073481 0.0683452 -0.703570 0.134632 0.274036
## Oceanic_LK_Sep 0.194544 -0.2000926 -0.559225 -0.006613 0.921143
## Oceanic_LK_Oct -0.040187 0.0297304 -0.402223 0.105522 0.359109
## Oceanic_LK_Nov 0.236117 -0.0281854 -0.188360 0.198519 0.025907
## Oceanic_LK_Dec 0.112173 0.1203014 0.146707 0.086058 0.221269# Checking if the PC axes are meaningful
eigenval <- CC.rda1$CA$eig # Here you will get the eigenvalues
sitecoord <- CC.rda1$CA$u[,1:2] # The site coordinates along PC1 and PC2
eig <- data.frame(eigenval)
eig$nb <- c(1:length(eigenval))
eig$prop <- eig$eigenval/sum(eig$eigenval)
eig# (Kaiser-Guttman)
par(mfrow=c(1,2))
barplot(eig$eigenval, main="Eigenvalues",las=2)
abline(h=mean(eig$eigenval),col="red") # average eigenvalue
legend("topright","Average eigenvalue",lwd=1,col=2,bty="n")
barplot(100*eig$prop,main="% of variance",las=2)
par(mfrow=c(1,1))
autoplot(CC.rda2, data = PCA.data, colour="Season",
shape="Sub_region",
loadings = TRUE, loadings.colour = 'black',
loadings.label = TRUE, loadings.label.size = 3,
loadings.label.vjust = -1.0,
loadings.label.colour="black",
frame = TRUE, frame.type = 'norm')+
facet_wrap(~Zone)+MyTheme+Season_colour+Season_fill
autoplot(CC.rda2, data = PCA.data, colour="Zone",
shape="Sub_region",
loadings = TRUE, loadings.colour = 'black',
loadings.label = TRUE, loadings.label.size = 3,
loadings.label.vjust = -1.0,
loadings.label.colour="black",
frame = TRUE, frame.type = 'norm')+
facet_wrap(~Season)+MyTheme
autoplot(CC.rda2, data = PCA.data, colour="Sub_region",
shape="Zone",
loadings = TRUE, loadings.colour = 'black',
loadings.label = TRUE, loadings.label.size = 3,
loadings.label.vjust = -1.0,
loadings.label.colour="black",
frame = TRUE, frame.type = 'norm')+
facet_grid(~Season)+MyTheme
autoplot(CC.rda2, data = PCA.data, colour="Season",
shape="Zone",
loadings = TRUE, loadings.colour = 'black',
loadings.label = TRUE, loadings.label.size = 3,
loadings.label.vjust = -1.0,
loadings.label.colour="black",
frame = TRUE, frame.type = 'norm')+
facet_wrap(~Sub_region)+MyTheme+Season_colour+Season_fill
Packages used
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#knitr::write_bib(c(.packages()), "packages.bib")
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